diff --git a/doc/tutorials/sequence_learning/iaf_psc_exp_nonlineardendrite_neuron.nestml b/doc/tutorials/sequence_learning/iaf_psc_exp_nonlineardendrite_neuron.nestml
new file mode 100644
index 000000000..64152d282
--- /dev/null
+++ b/doc/tutorials/sequence_learning/iaf_psc_exp_nonlineardendrite_neuron.nestml
@@ -0,0 +1,143 @@
+"""
+iaf_psc_exp_nonlineardendrite_neuron
+####################################
+
+
+Copyright statement
++++++++++++++++++++
+
+This file is part of NEST.
+
+Copyright (C) 2004 The NEST Initiative
+
+NEST is free software: you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation, either version 2 of the License, or
+(at your option) any later version.
+
+NEST is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License
+along with NEST.  If not, see <http://www.gnu.org/licenses/>.
+"""
+
+model iaf_psc_exp_nonlineardendrite_neuron:
+
+    state:
+        V_m mV = 0 mV         # membrane potential in mV
+        dAP_trace pA = 0 pA             # dAP trace
+        active_dendrite boolean = false
+        active_dendrite_readout real = 0.
+        dAP_counts integer = 0
+        ref_counts integer = 0
+        I_dend pA = 0 pA
+        I_dend$ pA/ms = 0 pA/ms
+
+    equations:
+        # exponential shaped postsynaptic current kernel
+        kernel I_kernel1 = exp(-1/tau_syn1*t)
+
+        # alpha shaped postsynaptic current kernel
+        #kernel I_kernel2 = (e/tau_syn2) * t * exp(-t/tau_syn2)
+        I_dend' = I_dend$ - I_dend / tau_syn2
+        I_dend$' = -I_dend$ / tau_syn2
+
+        # exponential shaped postsynaptic current kernel
+        kernel I_kernel3 = exp(-1/tau_syn3*t)
+
+        # diff. eq. for membrane potential
+        #recordable inline I_dend pA = convolve(I_kernel2, I_2) * pA
+        inline I_syn pA = convolve(I_kernel1, I_1) * pA - convolve(I_kernel3, I_3) * pA + I_e
+        V_m' = -(V_m - E_L)/tau_m + (I_syn + I_dend) / C_m
+
+        # diff. eq. for dAP trace
+        dAP_trace' = -evolve_dAP_trace * dAP_trace / tau_h
+
+    parameters:
+        C_m pF = 250 pF                    # capacity of the membrane
+        tau_m ms = 20 ms                 # membrane time constant.
+        tau_syn1 ms = 10 ms            # time constant of synaptic current, port 1
+        tau_syn2 ms = 10 ms            # time constant of synaptic current, port 2
+        tau_syn3 ms = 10 ms            # time constant of synaptic current, port 3
+        tau_h ms = 400 ms                # time constant of the dAP trace
+        V_th mV = 25 mV                    # spike threshold
+        V_reset mV = 0 mV                # reset voltage
+        I_e        pA = 0pA                    # external current.
+        E_L        mV = 0mV                    # resting potential.
+        evolve_dAP_trace real = 1        # set to 0 to stop integrating dAP_trace
+
+        # dendritic action potential
+        theta_dAP pA = 60 pA                            # current threshold for a dendritic action potential
+        I_p pA = 250 pA                                     # current clamp value for I_dAP during a dendritic action potential
+        tau_dAP ms = 60 ms                                # time window over which the dendritic current clamp is active
+        dAP_timeout_ticks integer = steps(tau_dAP)
+
+        # refractory parameters
+        t_ref ms = 10 ms                                                        # refractory period
+        ref_timeout_ticks integer = steps(t_ref)
+
+        I_dend_incr pA/ms = pA * exp(1) / tau_syn2
+
+
+    input:
+        I_1 <- spike
+        I_2 <- spike
+        I_3 <- spike
+
+    output:
+        spike
+
+    onReceive(I_2):
+        I_dend$ += I_2 * ms * I_dend_incr * 1E6 # XXX factor 1E6?!
+
+    update:
+        # solve ODEs
+        integrate_odes()
+
+        # current-threshold, emit a dendritic action potential
+        if I_dend > theta_dAP or active_dendrite:
+            if dAP_counts == 0:
+
+                if active_dendrite == false:
+                    # starting dAP
+                    dAP_trace += 1 pA
+                    active_dendrite = true
+                    active_dendrite_readout = 1.
+                    I_dend = I_p
+                    dAP_counts = dAP_timeout_ticks
+                else:
+                    # ending dAP
+                    I_dend = 0 pA
+                    active_dendrite = false
+                    active_dendrite_readout = 0.
+
+                    # the following assignment to I_dend$ reproduces a bug in the original implementation
+                    c1 real = -resolution() * exp(-resolution() / tau_syn2) / tau_syn2**2
+                    c2 real = (-resolution() + tau_syn2)*exp(-resolution() / tau_syn2)/tau_syn2
+                    I_dend$ = I_p * c1 / (1 - c2) / ms
+
+            else:
+                dAP_counts -= 1
+                I_dend = I_p
+
+        # threshold crossing and refractoriness
+        if ref_counts == 0:
+            if V_m > V_th:
+                emit_spike()
+                ref_counts = ref_timeout_ticks
+                V_m = V_reset
+                dAP_counts = 0
+                I_dend = 0 pA
+                active_dendrite = false
+                active_dendrite_readout = 0.
+        else:
+            ref_counts -= 1
+            V_m = V_reset
+            active_dendrite = false
+            active_dendrite_readout = 0.
+            dAP_counts = 0
+            I_dend = 0 pA
+
diff --git a/doc/tutorials/sequence_learning/sequence_learning.ipynb b/doc/tutorials/sequence_learning/sequence_learning.ipynb
new file mode 100644
index 000000000..5692bbfa9
--- /dev/null
+++ b/doc/tutorials/sequence_learning/sequence_learning.ipynb
@@ -0,0 +1,3902 @@
+{
+ "cells": [
+  {
+   "attachments": {
+    "image-2.png": {
+     "image/png": "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"
+    },
+    "image.png": {
+     "image/png": "iVBORw0KGgoAAAANSUhEUgAAA48AAABZCAIAAAARhIb3AAAACXBIWXMAABScAAAUnAHVi7b7AAAgAElEQVR4nO3deUCT9/048A85CAHCGe5DDjkERQ6Rw4uqKGqts1p76NZurT2sXbtu69bvunXftd927dau3a+10s7ac7ZWaz3wwnojNwFUCGc4cpCDEMhFIHny+yM1hnDleJInwPv1VxKSz/MBkjzv53O83256vR4BAAAAAADgkkhEdwAAAAAAAIBJQbQKAAAAAABcF0SrAAAAAADAdUG0CgAAAAAAXBdEqwAAAAAAwHVBtAoAAAAAAFwXRKsAAAAAAMB1QbQKAAAAAABcF0SrAAAAAADAdUG0CgAAAAAAXBdEqwAAMJVvyhre+uEK0b0AAIC5C6JVAACYSrdYdri8keheAADA3AXRKgAAAAAAcF0UojsAwNwlkStLG9p6+wflag1CSCJXxQb7v7RlFdH9AgAAAGz39vErHNEAk+GJEPKkuUcF+q5MiY1m+tncIESrYIzqdm51R29EgC9CiExyi2b6zQ9letKoRPdrttFh2PslZYeuN2i0WtPHR0a1k71k1usUSj+7XJebELkxM5novgAAALBdC09c3cE1feSdk9fuzUp+ees9tkUUEK2CMao7evefrzR9hEGn7VyR/ljBEohZ8cKXDn10vuJ4dRPRHXE5P1TdigrwIboXAAAAcKbDsOPVTVod9tS6nJggf2tf7pB1q3K1hi8d4kuHGrsFbJ7YEYcATiNXa/afr3z96I/d4gGi+zJLHK9pglAVAADAXFNSxz5h0+kPz7FViVz56cWaU7VsmVJtfDA7PvLAnu04HgUQ4lQtW6PVvfOLTUR3ZMbTaLXf3bg5/nFPGpXJ8EqKCHJ+lwAAAAAcJUUECQcVA0q1YVeGqS+vsB5Zkc5keFnVIG7R6vHqpo/OV/ClQ3g1CFzNDXbXtWbOigWxRHdkZitjd0vkSuNdTxp195qlO/LTGHQagb0CAAAA8PLSllWGHcMarfZ4VdP+85XGE59Gq714s2NHfppVDeITrfZIZG8eu6TSjOLSGiDQnvV5jyxPH1CqJUOqa82co5W3jBdGSs3olSaIVu11o6Xb9O5buzasSokjqjMAAADmCI1W28ITN3b3sbr47YL+v+5YmxEb7uiD0iiUHflpqVEhj33wnXFXcW0nj4BoVaPVvVdSBqHqrOHnRffzoscGB2TPj8yMi3iv5HqnUGr4URcsXbUb584fEyEUHuADoSoAAABHUAxrWvmSFr64VSBpFUhaeWKNVmf86RvfX3q2KK8g1RnnoNSokKSIoMZugeFum0BibQs4RKtCmfxCY5vpIww6bUt2SrCvN5PhOT+Uaf8hAFEKUuOq2nuN0WpVW69KMwrJAewhkauMtyP8Yf87AGBO02i1/UMq4aBCMqRUjYwihML9fZg+nkyG1wxdH6XDMDKJsNJL3WLZT+EpX9LCFwkG5JM9s4Uv/vhCFZVMWpYc44SO0Shk4+1eyaC1L8chWq1o7TG9uyol7p+PbqRRIDfWLOHt4W56t0ciS4adQHYYv+QcAAAsceh6vdOORSaRrJ2rtUozV1TH4dV28us6eVKFavwTqGRSVnxkZmxEZlx4VlwEgfGfVTgi6etHLjpzc7lcrbkTm4pbBZI2gURjcd7uWz19xaWVVAp56fwoh3bSjA3XITjElDd7+oy3aRTKW7s2QKg6i5FJbkR3YWYbnsP5/53v/525cazy9sW/7ia6IwDgoEMoPXyj0TnHemXbage1zOLwT7NazrBahlTDUzxtVIdVtPYYhsMKFydsyEhau2i+g7qEl8ZuwQsHT0nkyh6JzJ66TdPqEg20Cu6Ep3wJf8D2De71XYLi85VUMtkJa1iNbDgP4hBWckR31zImRQTBNPHsJlNO9f0CpjWq003/JIAfmUo9/ZMAAI6nw7Avr7I+uVBl7RRTaUNbaUPbhoyk5zcuC3fVAiJHym8Wl/60872itcdx0SqbJ/75v781q4Noj+oO7l8PXziwZ5u1WaVsZsN5EIehdeHg3VURpusSwKxEIcPYql20OozoLgAAgLPVdHCfP3jy3ZPXbF4NdYbV8uuDJ07VsvHtGC6+vdG471yFcFBhuMvi8B13rBM1TTiGqgYckfR/D19o4TupnJPlaxWMcIhWZ8pqEoALrU5PdBdmNh0G0SoAYG4539D27slrV5s4drbType8e/LqF1fqcOkVXr6+xvrw7A3TRNqsLr7oTuSKL/XIaG0nzxEtX2niFJ+v7Ojrd0Tj9sMh0BxQwEQbAAAAACbAEUlfP3rxVq8Ql9YkctU/T1z96ioLl9bs99nl2g/PlputkeNLhxw0vFrXyW/mihzRMkLows324gtV3WKZg9o3Ze3wMA7RKo0Ke6oAmFv++PXZ5a98RHQvAACurqaD96f/njOtx46Ljy9UHqu6jW+bNvjPj9Ufni1XDI+M/1F9l0Oi1dpOriOaNTrLavn4QqU927Ymw/QZsyi2f2iCRBBTwCFaxf1dCFyKD93D9K4j3sRzh3pkTBEN75mZTdAAFuACAKbWIZR+drkGr1FVUzLl8OeXa6/YvbTAHsWllfvOlU+2BJPF4Y9ocd5Tq0fIQcsATJ2saf64tEo8pJz+qdYwywPg70236uUwLAqmYRaUMBmeRPXEErd7hRK5UihTmC4hQghRSKT5YczVC+OJ6piBDhuz6tcD5iUAALPXieom+9eqTqZTKD1ScTMrLsIsKbgTaHVYcWllcWnlFM9p4orqu/j45jGt7eA6dP+W0feVtyhk0t6ifD8vj+mfbRmz853Z2XBaLnGy/NnbXxiqJb36wNptuQsddyAWh//oB4dNH6FRKN50dz8venJ4UNq8sKL0RGvjfduOixAik0h+Xh5J4UGbMpM3ZSWT3PDca797/9HKtt6pn1P6lydCfL2nbcrDfcybxDVH1KrbuUcqbpa39kwx0u9Jo1589UliM6xRKWNmM7RE77hiCWQytS01k4UKjU6vv9SJ5wZS4cAQQqhzQIljs90yld7ufnKH1BhmaSN50QEekBoFAIRqOrgldY7dv3/ldudpFntHngOrGIw3PDJaXFp14GL1tM9kcXCOVuucEqoaHL7RSCWT927I86LhczFgFm+IhxTeHgGWv9zeaFUzdqDbLLKxBJsnMhb2LKljOzRaHU+j1Wrk2n65qqOvv6SO/c7Jq5uXLPjdfSvx+vdMQYdh/XLVjZbuGy3dP1Td/uCJLXR3V0xVG+g9ZjBVNmU+Z+eTyJV/+PJMdQeXRqEsS54XGxKQGMaMDQ7wGTfJHujjSXjdiqaxM2LxIYFE9cTA2/oPrIE72Q0h5EfH8x2rVJERQh4UEo7N0igkZEc/B5UqKoVCo5Dd3CxtBN/LTgBmrtOsFgftix9zlLqWFcmxYf4MRx/IQDk8sv98xeeWJSXAfRy0tsOxi1bNfH2NRaWQ9hblu+NxBe4xNsKxNpaw9+StHZvi1YNq9VnBNHdabSe3TyYP9XP42+4PPyvISYhCCOkwTK7WCAbkdRzeufpWxfDI0YpblW29n+7Z7ohuGI+LEFIMj3SJpF9eZbUJJNUd3H8cv/qXB9bge7hopt8bj6yf7KcBlo0iU8e+TRXDLlQ41Fg4ZENG0ov3rgjxm36omFia0TGfFwrRhcESAm38i/nT3clubhlheKa/9iVhCKFwBh3HZq97e7hZ2U8dhh2tuHXhZvvtXqEhMSST4aXXI3x/WQBmN5Vm9FilM3ZB1XXyylu7789xxjiXTDVcfL7y62uWpiOo7+K38MVJ4fjUKr/VK6xz/KJVM59dqqWSyXs35Nt/rooYW9aBI5RmxFhRPcveaNVsDFJuZSiD6fVnWC0IoeXJMeWtPToMO13X8qvVS+zs1bTC/BnzQ8cMa21esuC3m1f+/YfLJ6qbuP2Dzx88+eVzD+JyPTHFcdNjwjZlJe94978dff3Hq5t+v2UlvsOrdHdq2rwwOxtJDGOa3jUtXUYsjkj6ZPH3DDrt8707nFkyzh4Xb3WY3k2NDiWqJ2BCKs3oM58cMxsRkchVCEGaYQCscLTyptNyS9d28JwQrUrkyuLSqm/LGix/iUozWs/h4xWt1nZwNXhv27LEJxeqqGTy0+ty7GzHLCcAm2fdAi17o1Wz45lFNtOqaus17DvbtTJDj1AZu6ukju2EaHVC3h7urz+0To/pT9Y2N3NFP1Td3pHv8NUwVDJ5R17am8cujep07X39i1wvfDFMoBtTozV2C4jtj4FcrXl831GdTv/eY5tTo0KI7o6lbrR0m95Nc71/9xz3+tGLE03ezeBQtV+uqu3kNXNFzTxRE1doyAr52kPrtmSn4NX+xVsd5a3dzVyReEhJIrkF+3hnxkXsyE9baPcHU69HpY1tjd2CW73CPpm8X65CehTo47koOnRLdsry5Bg8foMxBlXDW976QqpQIYSe37Ts8dXZuB9ijnDmKGAdhy+UyUMcOSvbJ5MXl1Yerbhl7QtZXfwHly3GpQ+NPX24tGODfefKw/wZdn5pLEuax6DTjJXMTtQ0/eFnqywvL2VvtGr4VBsFWrlh3LAEO5DhmZsQLVWoy9hdbQJJq0BibdSLoz9uLbhws109MvrVNZYTolWEkHHBjQ21yJyARqGkx4YZ92z1iGVsnig5IpjYXn16qUYiV7720LoZFKoWl1b2SO5mXb4/ZyFjJmewwpFMqSZ8wxlCSK7WGKZ6xsuOj3RyZ/BytOLWB2dvOKhxFof/y33fYWP39vZIZD0S2fHq24+vzv71xmX2tD+i0/7uixKzB/nSIb506Fx967rFCW/uLKKS8ZwB+/uxy2YnNWADjVZb3trjtMPxpIN1HP6GjCSHtT+0/3zF8eomG17L4vBFg4pgCzY0T61dIKmabue0Q+GSwaYwLeH7yp8ifpVm9MDFmifXLrXwtThHq1b9Phqt9seb7QihovREEslt9cJ4D3fK8Ii2pJadeO9yOztmMwadVrg44UR1U5doAJc32bT6ZHLDDYdeGtojNyHaGK2qRkaPVTW9vJXIaFWmVH91hVW4OAGv8SEnaO/rN6sWeM/COKI64yKuNHV+cbmura/fkMnB2mtd3OkwbMK5SzKJtHdDvvP7gws3NxTi670gMnhBZHBkoO+f/nsOx8ZVmhEM08eHBm7MSMqKiwgP8FFpRm/19O0vreT2D/7nx+pQP4Y91/wkN7el86My48IXRYcG+3oHeHsqhjUc0cA3ZQ0VrT3nG9rmBfk/h9+/5mozp6SOnR4TVt/lEjNIM1czV6TS2JJsxGZCh23n6hYPFJdWnapttu3lggE5q4u/fnGind2o4/AHidviPC/ILwuPK/YH8hYdr24yfs1+dK4iOz7SwoV89lYHOFffarxNJpGy4qz4fS7f6jRUgNiYmYwQ8qRR70mNRwidrmPrCZ15M678dcLAu1aHGSYX5gX5Rwb4OvpwttmyNMV0uP7Q9frqdqfuTDRzpOKWRqvdkG7v598JNFrt7V7hvnPlD//rkHEGBCH0ZGHOqpQ5Ha2+ffzKcwdOVHdwjUnH+uUEj2n5edHHj6F60qhv79owUxZGj/f46uzSvzzx71/d98y63BV4T52H+ft8/NT9x37/891rl2bGRYT6MeJCAu7LTvn2N49EBvoihA5eqrGnfSqZ/J9ntu1Zn7diQWxSeFCQj1dscMDqhfH7d29dEh+JEMJxH49ieOS1736kksmv7ijEq805S+r0euwOOmJHX/+HZ8ttDlUN6vHIDFDj3GwAZjJjI3BJtZ4aFfLy1gLjXR2G/WrfkbePX2Fx+NNe3tg1tiqUKSpa7w5N+3vRE8OtmME3LAOICvQ1LtbclJV8htUiHFTUdHIJnHqLCfY33OjHu5aDZEhpnAtWDo90iwc+v1LXwheTSaQX712Oe+ob9cjoZMtMGXRabLClqc6YDK+/7lj752/OGx/Z88kPz27I25GXRkj60oYuAZlEykmIdv6hrZX9hw/GP1i4OGFvUZ7zO+M6vrrKmqzMdyJOOxJs89bPN7x+5KJhM1x4gM/y5Jjda5a6fq6JKZAcmXciLiQgLmSCrxEGnbY9d9F7Jdd50qFB1bCvJ24Jxg1IJLe8xOiaDm6/QqnDMMuXvk3hnZNXhYOKZ9blxk/0GwGrOP/Ks1+O88kaIcTmiYtLKw0zwPZgcQSaUa09Neq5/YPOzLQ6Hi4DqwY78tM6hNJD1+sNd3UYZjwdNL7zwhQvtP3PJ1MNv3nsknHzDULo56syLH/5oGq4jN2N7gysGixLmufnRZcp1SW1bAKjVWNhjCE1ztmaXj96cfyDuYnRTxfmZMZF4HsshFCPRLbr399O+KP8pHn7n9xqeVNbslO6xAMHfvwpH7JGq3335LV3T16bHxr4xiNFyRFOjTCEg/LkiCC5WiO3+B9EJpFcIebIiA3fujT1Z0tTie7IT651S/pVtkzYcYfUWgz7odnGCdOvb9yc8HEPd6pAS7a52fFaJQoM01vVYEHukoLcuxs9ywVyJJBb/vJ184M9qVAdABnXZDtoosww8RXm54NLqFrV3nu04lZ8SMATa2FbFQ5wT6czrb4BKz6klrjdK9x/vvJKU6f9TTVxhfVdfHuGV2o7eU7IXDuZYF/vTFwnl17eWpAaGfxDdZNVA8Y2Rqu1nbzvyhtN0/HcszB+bVqC5S2cq28d1ekQQhsz766MJpNI69MTvy1rKG1s+5/773H+O97A+PXqnDzftR28Q54NcSEBfl74l9HC0e61S5kMz+8rb7cJJMYH2/v6LQ8Z8TKk1vClQ0X/96lVryK8lhWZRHq0IIvw6q+mMsL8tFaWvzO4Vu3R5kYqiLVxN+TBST5ZvyjIKkqyN+eaqfZ2z+tuyOZ+2sCDgkPwNAsYqpmH+jFwqdyo1yNMjyGE5GpNl1j2TVnDldudCKFfFGTa37h6ZPTVb0vd3NCrOwrx3bM1Z6lHnLpoFSGE7w6Thm5B8fnK6+wuvBpkcQT2RKvOT7NqKisuwrCwB0f3ZaekRIU8+sFhy+MHq6PV90+XtQv66zg802N40qjPrMuNsub3MSwDWBAZbDYfvSkz+duyBrlac7WZs3bRfGu7hwtjAnwfOs4TWO/9crMxWMEwvVSpqmrrff902bn61qZe4RfPPYjvRpOk8KDvfrsTr9Y83ak7V2SsXZTwZPH3HJEUr2ZtQKNQkiOCdq6wYiwfIcSg04gtu6rDsD9/c74mO+VnS1MJzHphyuZaVlSym5sb8vOw8e+5Pj1h/3nzEtuPr8neuz7XtgYnQ6OQ3NzcbO4nsE0TV2TY0vDgMnzSqnxT1vDmsUumj3i4U54qzHlkebr9jf/7dBlPOvTQssXpMXheKc1lzt8xaWGxG0vUdvKKSysrcM1pUN9l+zy+eEhZS3S0im+DrXzJiZqmEzVNVg11WX2uMs4FG4X4eb+ybbVVc8F86ZDhn7coOvT22FqUVDLJz8tDphwuqWUTFa12iX9KgG+WzBZfJJIbk+G1MTN5fhjz4X8d6u0f3Heu/M/bcS5nha9Ltzp+qG4iNlRFCIX4ebN54nuzknGZBHQoJsNLYrKgSq7WfHWVVdPB/ezZHcSGzsR6qjBneER76HqDRqv1pFGz46N2r822v5IFcAUypfp3X5ToMCw5IujnK3EY+xyPRqHsWZeHS5nu+i7BoesNIb7ez2+yK9kWMGVWr9sZR2Tgc7KubOspLq3CfUsTi2N7Uas6Ds809aGT+Xp6ZMbhuQxAh2H/KrleNm7cetq5Zbt2WZFJpIeXL95blG/tefc066dd/4dvNB6+0Tjhc641c+RqDSEJKVmdP10Gpc1zRvL2xDBmSlRwQ5fgx5sdrhytlrG7XvryjOlKZaIsjAqpaO2pbufmJrr6RquLf90tU6qvNHHeOXnNuPmdzRO/fvTiFEVxZz0yifTi5hUvbl4hkSsZdBqNgkMmP+AKlJqRZ/9znNs/yGR4vfPovXit5np4+eKHly/W65FEruzo6//scu27p64dKqt//5eb7cn9PKLV/eXb85he/8q21WZ1GYE9EsKYZBLJabWsEEJ0W6eJTN1o6d5/vsIR+cvUI6P1HIFt0WptB5EDq5lxEfND8ZwJ/Oh8pWmo6kmj7i3KL0xLmHZjiV1DUzoMUw6PtAqsK5+FECqpZU/7nBGtrrSxzaZ+2UWu1pTebEMIxYcGMnG6XJtWsI83QkiqUBnW8rqgHonspa/GhKrJEUEvby049MLDaTHOLshkSF7DsmNuxZn8vOhbslO+//0u00/jqdpmO7OizA5MhhfuoaoOw9g88RlWy8VbHc5fPzeXKYZHnv742M2ePibD6z/PbLNqbZgl3NxQkI9XbmL0/ie3PpC3SDAg/+3nJSN21KLcd668SzSwPj1xVeqcTieHOwad5syhhECGZ37SPDsbudLE+fBsueNS7dp8wqoktChAAq6L1ipaez4uvbsGzJNGPfzizl0rMyzZA23veeJY1e0LN9tfe2id5XtH2DxRh1CKEPrz9jUP5C2a8Dnb//lVq0ByqpbthOK/Zt48dml4RIsQ+vlK65ZF2qO3fxAh5E4hu+Yafx2GPXfghOkSk4eXp7+0ZSVRE/H5SfOWJcd8frm2KD3R8jxcxGIyvF7Ztvq5AyeMj3xT1nhv1gICuzT7CGWKd09du3izw3hZRaNSnDnAM5fJ1ZqnPj52q6ePyfA6sGeboz+YT6xZ+l35zd7+wfKWbttizS7RwOeX67w93H+9cdmEuR5HtZjh8bm8aMdmWXER42d7HSQzNmJekL89Lfx4s724tIrNE+HVpfHqbS0P+2hB5odnK8RDBOQEoFEpBbheyH1XMSYbzPMbl0Uz/Sx8rdXR6ivbVncIpWXsLuNCCrla89vPSz7ds93CBNoldS0IITKJVJg26bLUooykVoGktpPbJ5OHTvffFQ8pDSv6cxKi7LkOUAyP/P2Hy6dq2QihhVEhm5dYGklcuNneNyCnu1NtW0d1o6W7hS9CCC2yuGr819fq9Xp9TLC/Iyplj/ftjUbTtaqbMpNf3LyC2DWjL21Zef8/vnrh4Kmvfv3gTKlfuiolLjMuwri7kyOScvsHcd9rOWcZ8rUZl1sYjGi1xJYamSNkSvWTxcfYPFGQj9eBZ7YbU1Y7TsidPeDdti7p65PJdRimGB7Z9MbBCZ+w71z5vnPlCKGrf3vKxRO2uCB8cx5NLSverm1A5+pbi0sr2/v68erPhPgDQ6wuQVG61dHq/TkLtRj2wZlysy83J8iKi1iIX21zbv+g6d61zLiIh63ZJWl1tGooo6fDsI/OVxpHdHUY9tvPS7769YPhAT5TvxzT60/XsRFCOQlRU3z+i9IT/326TK9Hp+tafrV6yWRPM+D2D759/ApC6JVtqy2MVgUDcsNb0/BtxR8YYnXyz9a3GGprRTP9/vXLzZYPc35b1lDZ1hvI8Jw2WjUeFyGE6fUDCnVlW8/X1+oNJ9TH7pnmNzX6x4krGKYvSk+cOlqdojoAQiia6W9hcpmGsZMjW7JTaAQlFzOKDQ54eWvB60cvvvTVmfd+ee9MWfiYnxhtjFblak0zVwTRKl7+57/nxn+b6/XI9bfizXQSuXL3/u87+vqDfLw+3bPdzlEuCxn3wsJ6U9eUGRexISPpDKvF0QdKjgiyZ9XB6bqW4tJK52wdrufwi2wqwbgjL21Ui314ttyYsMg58L3kkCpUpjO0MVZ+Udh4jieTSHuL8mRKtXGPlESu/PZG42/uXT71C6vbueIhJUJo/ZT/s8hA39SokNu9wpI69rTRqv7O4Inlp6W3frg84ePuFPJ9S1J+d99Kq6Z+DMcnWZCddbLjkkmkFzcvX5USa/lBEUKk6X7fKaoDIITe3rWhKCNpsp8ayVTDpoXj0mPCsucTVrjB1I78NNGQ8uPSyi1vffHSllUulcd0MtnzoxAqN95t65MULrYiSzHujtzm9Q7acr3OFstHdNi/bthb5QUv6uHhyS7MYqPCndnPx7Pm+cyliWPRoOLxj452iwdC/Rj/eWab5fN6lpisDhaG6d8/XWa4bfPXUW5i9GS1c9J++x5C6PlNyx5fDcUCbLcxM/lqE0epGXHoUTZkJMXZuuxEKFP8q+SaUOakSfbTrJbf27qCbueKdK0O+/Bc+bAT1+LjW7SIIxowvbvEyhFxu0aknt+47FRts3HFz5GKm7/emD/1f8KQZpVCJq2ZLjvV+vTE273CNoGkVSCZOjmlYRUsjUJZaWW0hxByp5C9PWj+Xh5JEcHpMWFF6Yk2zPgYLsvWWRl2uLkhL5p7RIDvkvjIB/IWTVjAcEJdogEM0yOEplhKgSPNiFYgu1smJMwfn+IxuNhblBcfEvC/31144eDJZckxD+Qtig32d+WVrFTymD/dqJbgJZXbUy39vuBLh273Ctv6JIIBOdPHK5JB6yOTfpNPTI658VSa0W9KLox/nMnw+mDXumnnfGYxTK8fUg0bbg/euaHSjBrHoek0qunUxJUmznMHjiOEPtr9s2XTLTQSDip+9eF3vf2DAd6e7z52rw+dNn54m0GnmX5jWNX+vnPlbJ543eKE1KjQEF9vdwpZphq+1dP3TVmDIfXhfdkpZvHxOyevfX65FiFU/dbemTLlMlutSondkJF0pGLiwnW4yIyLMC2Haa0QP+9vXniEzRM188TNXBGbJzLsIXEQmVJd08HLSYiy7eWPFmSO6nQfni13zlr8jNhwHAuuojuRkpG1kzB2fZgZdNo9qfGGABQhJFdrGrv7pl69+rcHC//2YKEljT9WkPVYQZYlzzSkRrs/NzVouvSoGbHhUxeitUG3eEA8pHSnkH81+VU4vset6eQihBLCmKsXThorfPL0NrwO1yEcs5pngR35YhxhQ0ZS9vzITy/WHLreYFjUT6NQYkP8GR7mi1mjmH5/3r6a2FB7QeSYvx7hmWstdODH6v3nK00zQri5udHtKHuNO08a9eHl6cba0wapUSFvPLJ+LoeqCKG+Afn4qm9vHrtkTLb/8tZ7Hl6+2LbGr9zuNFErYJ4AAAsJSURBVJzdpQrVI+8dmvA5n+/dYeGWhvH0esTi8FmTVEjfnLXg1QdcN+UfQAg9sSb78u1O05zT+LpvyYIQ+6pYBTI8lyXHGC6clMMjTVwRmy9i88RNXFGHA1aysjh8m6NVhNATa7K1OsywnNrRsuIi8K3maZa+Y8JpkynYe75ZvfButIoQut0rtPmLyWY1HVwqeapg0cFH5yGEtuYsnDZWxveITxXmOKcwrH7sLhWa69VAZzK8Xtqyam9RfnVH742WHolcKVNMMLstU6q1GEZstGpIN2HkmikgzLx9/MpXV1lmD+r1ek8P11ov+NKWlVGBvqWNbcJBxeJ5YSsWxGzISHKdeYCZwlCOnEahJNqUGxLf9nevXZocEVTe2tMmkPTLVYrhEU8aNSLAJ21e2H1LFkxYTkI4qEAIJYUHuZNd6GpqzgoP8Pnj1oJ3Tl4VDMinf7aVnt+0DN+sQV4e7tnzIw1rS0Z1OkPMyuaJ2DxxM1eE4bFh056iVgZPFeZoMcw0D5SD4F7CSjw45qLF2hIS9n6ezebNVQ5eoTJej0QmHlLuyE+z8wLLZoZY+fHpFtfie8T4kIDCNCetd+RKh0zvhvm76EiVJ426KiVuVYpL5000C56GXaDUwtR6JLLxoapBmsUpLJyDTCLtWpmxy4mJ52aE8AAfqyZ2Ktt6EEI78tMsufzekZ9m2HfroPaDfLzuz1loVURS3d6LEHq2KM+ei3ncp+DmsnWLE6QK1bunruO74PIXqzIfK3DgaZdKJi+KDjUm6mnhiw0xazNPxOaJbU7kzOLw2TyxVbU/zbi5oSfXLh3V6g5eqrG5kWmlRIbgu2gVjTvfUcjWDSXYG60GjC0H3K9Q2dmgtaKZfsR+s7y5s+jNnUXOPOKFvzzhzMOZfcWQSU4Z0Z2lPMYWXFENO/vqzlqTTcIihHaugLhwttHrUXUHl+5OfWKNQ6aqHN1+e19/v1y1MDoU3ySRwE4PLVvsTiG/duQiXgsuV6XE7Vmf58yTUVJ4UFJ40JbsFIRQl2iAzRM1cUVsvpjNE8mUw5a3ox4ZbegW2BOtIoTcKWTDCOuXV+rsaWcKmXHhdHecd4uaVbbrl6usWqZlb7QaP3ZvULvAsRnLgPOZZTOVTjTJDmar2EkSZ+5ameEiqSEAjloF4gGF+ok12f7eDkkv6uj2DQO3e4vyHNE4sMf9OQvp7tSDl2rYPKuLX5ry86L/8p6sRwuyLEnC4yAxwf4xwf6GjDr8gSE2T8zmiZq5omae2LDQZWosDv9BK2ckxvOkUZ8qzBnV6r4pa7CzqQllxuI8sIoQMluhZ0n9KlP2RqtmJUB8PGdGnnZguT7ZmPVGFqZoBbND2rywe7MWmBaJZdBpz29cZu38L5gRksKDHDpV5ej2d67IgCF/l7UhIyk5Iuivhy9MMWMzNSbD6/lNywwDnC4i3N8n3N/HkD9RqlAZYlZD8DpZeoF6Dl84qLB/7aIPnfZUYY5Wh+GedSEuJMDa9FI2sHZTgb3RqtnKg+FRFy1zD/Di6e5ae2tmHBqFonH55aqm3nhk/dq0+YaahMkRwbkJ0VCIEgBgg9jggIPPPnCG1XKG1XK1iWP5CxdGhWzITNqQkcRkOGk3sw0CvO+mF1AMj7B54maukM0TN/PE7X0S49P4A0MsW8sEmAlkeP5p2z1SherirQ77W0MIRTP9UqNCViyIccHibfZGq2YLEUZGZ9JpGADnc3Olhb9n24QChSWLrij04HCEUPco6m6yd0/r7PZAaoS3O+xGBw5BJpGsKlbpgkhubpsyk4vSE680cS40tl+81W42Q2smIza8MC2hKCPRlePU8bw93JfERxhHKNk8cU0Ht6aD29jdJ5ErrzZxcIlWEUJkEul3962ku1NNszNZyM3NLSE0MDGcmRgWlBjOTAhjOi21kYf1i2LhWxVYB3ZZ2SnAm86XOq8YydSKEnCrAQ0AcLSXtxYQ3QV8kEmk1QvjVy+M12GF1e3c9r7+foVKMqTkS4f8vOlMhlegNz2K6ZeXGO2Cg3w2SI4ISo4IMmQs4Yik+O7wiQz0fWpdjhbDztW3TvvkIB+vxPCgxDBmYjgzMYxpYbF63NmQIAKiVWAds9F0YK1RHcH1qwAAwEWQSaTcxOjcxGiiO+I8scEBuBdcjAnyN6S1Gr8kgEommYSnQfPDAv1d4AIgyPpluzhEq0yGl+MqVQBXox6BxR528aa527UnFgAAABgrIYz5yvbVHNEARyQlk0gJYYFp88IWRYemzQt1wWrkymGNtS/BIVq1OU0umImszegLzPiZlJtTwWcHAAAAHpgMrwN7tnGEA6lRIS64F1ZpUj0qw/oMWThEHr6Q0mhWw8ZWm4ti+hLUkVlicczdcpG3e4XfV94isDMAAABmDSbDK3t+pAuGqmXsrhb+3cQITIZ1ZVcRLmOrGIZD8VzgmjRa7dWmTuNdBp02szZmuqCchKjPLtca77525GITV7Q9d1Gon/fs2E8AAAAAIITkao1ErjxR03zoer1pJbMl8VYXl8EhWg329TYmkIeZzdmkva///ZIy09Ij2da/w4CZ2JCAjNgIFodnuKvDsMM3Gg/faDTczY6PPLBnO3G9A4R54eDJ4TsZAFOjQp7bkE9sfwAAwGaP7ztS3cGd8Edp88KWzo+ytkEcotX5oYGN3QLDbTZPLJErYfht5jpe3XSiukmj1UnkSr50yOyna9PmE9Kr2STc32fXinRjtEqsPvmwWgsVPfAU5etJsSnLW3lrD+wBAADMevlJ0WH+DGtfhUO0mhEbblx7p8OwFw6eeucXm6ytAAtcBE86ONn10D0L4wsX45PQeI4rXJzw9Lqc/ecrie4IapMq+lUQIeEpxNuDQoIsbwD1SGRvfH/JeHfr0tT1OCWEB2Dm2r126RNrltrwQhyi1dyEaDKJZFyR0NgtKPq/T1elxIb4MXw9aREBvi5V1RfYxpNGfawgiwbJVnGyZ32evxf9kwvVxGZ/WzGPmNTQAMx6crXmRku38W72fFhGBeY0Bp22c0X6U4U5ZJIt+/txiFZD/Ly35S40LrxDCOkwzJiiNjs+EqLVmS6a6ffaQ+syYsOJ7sis8vDy9PtzF1a09vRKBofUP5U/jQiAlAtzkUypHh69O8htwxYEAABwHfdlp2TdKTzrSXOPZvplx0cy6DSbG8SnltXT63Iu3uyAGgGzQESAr3ErFZlMimb6pceEbchIsu1iCEyNRqGsSokjuheAeJVtvfo7uVUCGZ47V8zsWvAAgDkO92FKfKJVQ07aJ4u/F8oUuDQIiLIlOwXGwgFwssq2XuPtJ9Yspbu7XLpEYC3BgNz07pI4GC8HwHa4DZjFBgecevmxl7asSo0KsWewFwAA5pqq9p+i1VA/xgN5i4jtDMBFRVuP8faqlFjTsiAAAGvhM7ZqQKNQdq3M2LUyA8c2AQBgduuTyXskMsPtp9fluMNexlnBOF7u5oaeLYLsuQDYBRYjAgAAkYxhTTTTD9bhzA6iQUW3eMBwe93ixOSIIGL7A8BMB9EqAAAQqfLOlPGe9bmwnXF2MF6BkEhue9bnEtsZAGYBN71xJyoAAAAA7PbKN+dPVDchhLZkp7z20DqiuwPAjAfX8QAAAACeqtp6EUIUMumZdTCwCgAOYGwVAAAAAAC4LhhbBQAAAAAArguiVQAAAAAA4LogWgUAAAAAAK4LolUAAAAAAOC6IFoFAAAAAACuC6JVAAAAAADguiBaBQAAAAAArguiVQAAAAAA4LogWgUAAAAAAK7r/wNipCyM7kH7OwAAAABJRU5ErkJggg=="
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "NESTML sequence learning network\n",
+    "================================\n",
+    "\n",
+    "*Much of this text is directly based on and excerpted from the PhD thesis of Younes Bouhadjar [1]_.*\n",
+    "\n",
+    "<a name=\"introduction\"></a>\n",
+    "\n",
+    "Introduction\n",
+    "------------\n",
+    "\n",
+    "In this tutorial, a neuron and synapse model are defined in NESTML that are subsequently used in a network to perform learning, prediction and replay of sequences of items, such as letters, images or sounds [2]. Sequence elements are represented by Latin characters (A, B, C, ...).\n",
+    "\n",
+    "![image.png](attachment:image.png)\n",
+    "\n",
+    "The architecture learns sequences in a continuous manner: the network is exposed to repeated presentations of a given ensemble of sequences (e.g., {A,D,B,E} and {F,D,B,C}). At the beginning of the learning process, all presented sequence elements are unanticipated and do not lead to a prediction. As a consequence, the network generates mismatch signals and adjusts its synaptic strengths to minimise the prediction error.\n",
+    "\n",
+    "There is a distinction between a training phase and a replay phase. During training, the network is exposed to the sequences that we want it to learn. In replay mode, the network autonomously replays learned sequences in response to a cue signal, and synaptic weights are fixed.\n",
+    "\n",
+    "In general, the sequences can be \"high-order\" (similar to those generated by a high-order Markov chain), where the prediction of an upcoming sequence element requires accounting for not just the previous element, but for (parts of) the entire sequence history or context. Sequences within a given set of training data can be partially overlapping; they may share certain elements or subsequences (such as in {A,D,B,E} and {F,D,B,C}), and the same sequence element (but not the first one) may occur multiple times within the same sequence (such as in {A,D,B,D}).\n",
+    "\n",
+    "Network structure\n",
+    "------------------\n",
+    "\n",
+    "The network consists of a population E of $N_\\text{E}$ excitatory neurons, and a population I of $N_\\text{I}$ inhibitory neurons. The neurons in E are randomly and recurrently connected, such that each neuron in E receives $K_\\text{EE}$ excitatory inputs from other randomly chosen neurons in E. These \"EE\" connections are potential connections in the sense that they can be either \"mature\" (\"effective\") or \"immature\". Immature connections have no effect on target neurons. (More details to follow in the section about the synapse model below.)\n",
+    "\n",
+    "![image-2.png](attachment:image-2.png)\n",
+    "\n",
+    "The excitatory population E is subdivided into $M$ non-overlapping subpopulations $M_1, \\ldots, M_M$, each of them containing neurons with identical stimulus preference (\"receptive field\"). Each subpopulation $M_k$ thereby represents a specific element within a sequence. The number $M$ of subpopulations is equal to the number of elements required for a specific set of sequences, such that each sequence element is encoded by exactly one subpopulation.\n",
+    "\n",
+    "All neurons within a subpopulation $M_k$ are recurrently connected to a subpopulation-specific inhibitory neuron $k$ in I. The inhibitory neurons in I are mutually unconnected. The subdivision of excitatory neurons into stimulus-specific subpopulations defines how external inputs are fed to the network, but does not affect the potential excitatory connectivity, which is homogeneous and not subpopulation specific.\n",
+    "\n",
+    "#### External inputs\n",
+    "\n",
+    "During training mode, the network is driven by an ensemble $X = \\{x_1, \\ldots, x_M\\}$ of $M$ external inputs, representing inputs from other brain areas, such as thalamic sources or other cortical areas. Each of these external inputs $x_k$ represents a specific sequence element (\"A\", \"B\", ...), and feeds all neurons in the subpopulation $M_k$ with the corresponding stimulus preference. The occurrence of a specific sequence element $\\zeta_{i,j}$ at time $t_{i,j}$ is modeled by a single spike $x_k(t) = \\delta(t − t_{i,j})$ generated by the corresponding external source $x_k$. Subsequent sequences $s_i$ and $s_{i+1}$ are separated in time by an inter-sequence time interval $\\Delta T_\\text{seq}$.\n",
+    "\n",
+    "During replay mode, we present only a cue signal encoding for first sequence elements $\\zeta_{i,1}$ at times $t_{i,1}$. Subsequent cues are separated in time with an inter-cue time interval $\\Delta T_\\text{cue}$\n",
+    "\n",
+    "In the absence of any other (inhibitory) inputs, each external input spike is strong enough to evoke an immediate response spike in all target neurons in $M_k$. An external input strongly depolarizes the neurons and causes them to fire. To this end, the external weights $J_\\text{EX}$ are chosen to be supra-threshold. Sparse activation of the subpopulations in response to the external inputs is achieved by a winner-take-all mechanism implemented in the form of inhibitory feedback.\n",
+    "\n",
+    "#### Neuron model\n",
+    "\n",
+    "The dendrites are grouped into distal and proximal dendrites. Distal dendrites receive inputs from other neurons in the local network, whereas proximal dendrites are activated by external sources. Inputs to proximal dendrites have a large effect on the soma and trigger the generation of action potentials. Individual synaptic inputs to a distal dendrite, in contrast, have no direct effect on the soma. If the total synaptic input to a distal dendritic branch at a given time step is sufficiently large, the neuron becomes predictive. This dynamic mimics the generation of dendritic action potentials (dAPs): NMDA spikes which result in a long-lasting depolarization (∼50-500ms) of the somata of neocortical pyramidal neurons.\n",
+    "\n",
+    "The temporal evolution of the membrane potential is given by the leaky integrate-and-fire model:\n",
+    "\n",
+    "$$\n",
+    "\\tau_{\\text{m},i} \\frac{d V_{\\text{m},i}(t)}{dt} = -V_{\\text{m},i}(t) + R_{\\text{m},i} I_i(t)\n",
+    "$$\n",
+    "\n",
+    "with membrane resistance $R_{\\text{m},i} = \\tau_{\\text{m},i} C_{\\text{m},i}$, membrane time constant $\\tau_{\\text{m},i}$, and total synaptic input current $I_i(t)$.\n",
+    "\n",
+    "The total synaptic input current of excitatory neurons is composed of currents in distal dendritic branches, inhibitory currents, and currents from external sources. Inhibitory neurons receive only inputs from excitatory neurons in the same subpopulation. \n",
+    "\n",
+    "Total synaptic input currents:\n",
+    "\n",
+    "- excitatory neurons: $I_i(t) = I_{\\text{ED},i}(t) + I_{\\text{EX},i}(t) + I_{\\text{EI},i}(t)$ for all $i \\in E$\n",
+    "- inhibitory neurons: $I_i(t) = I_{\\text{IE},i}(t)$ for all $i \\in I$\n",
+    "\n",
+    "Individual spikes arriving at dendritic branches evoke alpha-shaped postsynaptic currents. All dendritic input currents $I_{\\text{ED},i}(t)$ evolve according to\n",
+    "\n",
+    "$$\n",
+    "I_{\\text{ED},i} = \\sum_{j\\in E} (\\alpha_{i,j} \\ast s_j)(t - d_{ij})\n",
+    "$$\n",
+    "\n",
+    "with \n",
+    "\n",
+    "$$\n",
+    "\\alpha_{i,j}(t) = J_{i,j} \\frac{e}{\\tau_\\text{ED}} t e^{-t / \\tau_\\text{ED}} \\Theta(t)\n",
+    "$$\n",
+    "\n",
+    "and\n",
+    "\n",
+    "$$\n",
+    "\\Theta(t)=\\begin{cases} \n",
+    "1 & \\text{if $t \\geq 0$} \\\\\n",
+    "0 & \\text{else}\n",
+    "\\end{cases}\n",
+    "$$\n",
+    "\n",
+    "All external, inhibitory and excitatory input currents $I_{\\text{EX},i}(t), I_{\\text{EI},i}(t), I_{\\text{IE},i}(t)$ evolve according to\n",
+    "\n",
+    "$$\n",
+    "\\begin{align*}\n",
+    "\\tau_\\text{EX} \\frac{I_{\\text{EX},i}}{dt} &= -I_{\\text{EX},i}(t) + \\sum_{j\\in X} J_{i,j} s_j (t - d_{i,j})\\\\\n",
+    "\\tau_\\text{EI} \\frac{I_{\\text{EI},i}}{dt} &= -I_{\\text{EX},i}(t) + \\sum_{j\\in I} J_{i,j} s_j (t - d_{i,j})\\\\\n",
+    "\\tau_\\text{IE} \\frac{I_{\\text{IE},i}}{dt} &= -I_{\\text{EX},i}(t) + \\sum_{j\\in E} J_{i,j} s_j (t - d_{i,j})\\\\\n",
+    "\\end{align*}\n",
+    "$$\n",
+    "\n",
+    "The dendritic current includes an additional nonlinearity describing the generation of dAPs: if the dendritic current $I_\\text{ED}$ exceeds a threshold $\\theta_\\text{dAP}$, it is instantly set to a the dAP plateau current $I_\\text{dAP}$, and clamped to this value for a period of duration $\\tau_\\text{dAP}$. This plateau current leads\n",
+    "to a long lasting depolarization of the soma.\n",
+    "\n",
+    "The NESTML model description for the neuron can be found in the file ``doc/tutorials/sequences/iaf_psc_exp_nonlineardendrite_neuron.nestml``.\n",
+    "\n",
+    "#### Synaptic plasticity model\n",
+    "\n",
+    "Excitatory connectivity between excitatory neurons (EE connectivity) is dynamic and shaped by a Hebbian structural plasticity mechanism mimicking principles known from the neuroscience literature. All other connections are static. \n",
+    "\n",
+    "The dynamics of the EE connectivity is determined by the time evolution of the permanences $P_{i,j}$ ($i, j \\in \\text{E}$), representing the synapse maturity, and the synaptic weights $J_{i,j}$. Unless the permanence $P_{i,j}$ exceeds a threshold $\\theta_\\text{P}$, the synapse $j\\rightarrow i$ is immature, with zero synaptic weight $J_{i,j} = 0$. Upon threshold crossing, $P_{i,j} \\geq \\theta_\\text{P}$, the synapse becomes mature, and its weight is assigned a fixed value $J_{i,j} = W$.\n",
+    "\n",
+    "Overall, the permanences evolve according to a Hebbian plasticity rule: the synapse $j \\rightarrow i$ is potentiated, that is, $P_{i,j}$ is increased, if the activation of the postsynaptic cell $i$ is immediately preceded by an activation of the presynaptic cell $j$.\n",
+    "\n",
+    "A homeostatic mechanism controlled by the postsynaptic dAP rate regulates synapse growth based on the rate of postsynaptic dAPs. This form of homeostasis prevents the same neuron from becoming predictive multiple times within the same set of sequences, and thereby reduces the overlap between subsets of neurons activated within different contexts.\n",
+    "\n",
+    "Permanences $P_{i,j}(t)$ evolve according to a combination of an additive spike-timing-dependent plasticity (STDP)\n",
+    "rule (Morrison et al., 2008) and a homeostatic component:\n",
+    "\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "P^{-1}_\\text{max} \\frac{dP_{i,j}}{dt} &= \\lambda_+ \\sum_{\\{t_i^\\ast\\}'} x_j(t)\\delta(t - t_i^\\ast - d_\\text{EE}) I(t_i^\\ast, \\Delta t_\\text{min}, \\Delta t_\\text{max}) \\\\\n",
+    "&- \\lambda_- \\sum_{\\{t_j^\\ast\\}} \\delta(t - t_j^\\ast)\\\\\n",
+    "&+\\lambda_\\text{h} \\sum_{\\{t_i^\\ast\\}'} (z^\\ast - z_i(t)) \\delta(t - t_i^\\ast) I(t_i^\\ast, \\Delta t_\\text{min}, \\Delta t_\\text{max})\n",
+    "\\end{align}\n",
+    "$$\n",
+    "\n",
+    "The first term on the right-hand side corresponds to the spike-timing-dependent synaptic potentiation triggered by the postsynaptic spikes. The indicator function $I(t^\\ast_i, \\Delta t_\\text{min}, \\Delta t_\\text{max})$ ensures that the\n",
+    "potentiation (and the homeostasis; see below) is restricted to time lags $t_i^\\ast - t_j^+ + d_\\text{EE}$ in the interval $(\\Delta t_\\text{min}, \\Delta t_\\text{max})$ to avoid a growth of synapses between synchronously active neurons belonging to the same subpopulation, and between neurons encoding for the first elements in different sequences:\n",
+    "\n",
+    "$$\n",
+    "I(t_i^\\ast, \\Delta t_\\text{min}, \\Delta t_\\text{max}) = \\begin{cases} \n",
+    "1 & \\text{if $\\Delta t_\\text{min} < t_i^\\ast - t_j^+ + d_\\text{EE} < \\Delta t_\\text{max}$} \\\\\n",
+    "0 & \\text{else}\n",
+    "\\end{cases}\n",
+    "$$\n",
+    "\n",
+    "Note that the potentiation update times lag the somatic postsynaptic spike times by the delay $d_\\text{EE}$, which is here interpreted as a purely dendritic delay (Morrison et al., 2007).\n",
+    "\n",
+    "The potentiation increment is determined by the dimensionless potentiation rate $\\lambda_+$, and the spike trace $x_j(t)$ of the presynaptic neuron $j$, which is updated according to\n",
+    "\n",
+    "$$\n",
+    "\\tau_+ \\frac{dx_j}{dt} = -x_j(t) + \\sum_{t_j^\\ast} \\delta(t - t_j^\\ast)\n",
+    "$$\n",
+    "\n",
+    "The trace $x_j(t)$ is incremented by 1 at each spike time $t^∗_j$, followed by an exponential decay with time constant $\\tau_+$. The potentiation increment $\\Delta P_{i,j}$ at time $t^∗_i$ therefore depends on the temporal distance between the postsynaptic spike time $t^∗_i$ and all presynaptic spike times $t^\\ast_j \\leq t^\\ast_i$ (STDP with all-to-all spike pairing [Morrison et al. 2008]). \n",
+    "\n",
+    "The second term on the right-hand side represents synaptic depression, and is triggered by each presynaptic spike at times $t^\\ast_j \\in \\{t^\\ast_j\\}$. The depression decrement is treated as a constant equal to 1, independently of the postsynaptic spike history. The depression magnitude is parameterized by the dimensionless depression rate $\\lambda_-$.\n",
+    "\n",
+    "The third term corresponds to a homeostatic control triggered by postsynaptic spikes at times $t^\\ast_i \\in \\{t^\\ast_i\\}'$. Its overall impact is parameterized by the dimensionless homeostasis rate $\\lambda_\\text{h}$. The homeostatic control enhances or reduces the synapse growth depending on the dAP trace $z_i(t)$ of neuron $i$, the low-pass filtered dAP activity updated according to\n",
+    "\n",
+    "$$\n",
+    "\\tau_\\text{h}\\frac{dz_i}{dt} = -z_i(t) + \\sum_k \\delta(t - t^k_{\\text{dAP},i})\n",
+    "$$\n",
+    "\n",
+    "Synapse growth is boosted if the dAP activity $z_i(t)$ is below a target dAP activity $z^\\ast$. Conversely, high dAP activity exceeding $z^\\ast$ reduces the synapse growth.\n",
+    "\n",
+    "While the maximum permanences $P_\\text{max}$ are identical for all EE connections, the minimal permanences $P_{\\text{min},i,j}$ are uniformly distributed in the interval $[P_{0,\\text{min}}, P_{0,\\text{max}}]$ to introduce a form of persistent heterogeneity.\n",
+    "\n",
+    "The NESTML model description for the synapse can be found in the file ``models/synapses/stdsp_synapse.nestml``.\n",
+    "\n",
+    "#### Connectivity\n",
+    "\n",
+    "The sequence processing capabilities of the proposed network model rely on its ability to form sequence specific\n",
+    "subnetworks based on the skeleton provided by the random potential connectivity. On the one hand, the potential connectivity must not be too diluted to ensure that a subset of neurons representing a given sequence element can establish sufficiently many mature connections to a second subset of neurons representing the subsequent element.\n",
+    "On the other hand, a dense potential connectivity would promote overlap between subnetworks representing different sequences, and thereby slow down the formation of context specific subnetworks during learning.\n",
+    "\n",
+    "During the learning process, the plasticity dynamics needs to establish mature connections from $\\mathcal{P}_{i,j}$ to a second subset $\\mathcal{P}_{i,j+1}$ of neurons in another subpopulation representing the subsequent element \n",
+    "$\\zeta_{i,j+1}$. For $p \\geq 0.2$, the existence of the divergent-convergent connectivity motif is almost certain ($u \\approx 1$). For smaller connection probabilities $p < 0.2$, the motif probability quickly vanishes. Hence, $p = 0.2$ constitutes a reasonable choice for the\n",
+    "potential connection probability.\n",
+    "\n",
+    "## Getting started\n",
+    "\n",
+    "First, import the required modules and set some plotting options:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "              -- N E S T --\n",
+      "  Copyright (C) 2004 The NEST Initiative\n",
+      "\n",
+      " Version: 3.8.0-post0.dev0\n",
+      " Built: Sep 26 2024 22:44:51\n",
+      "\n",
+      " This program is provided AS IS and comes with\n",
+      " NO WARRANTY. See the file LICENSE for details.\n",
+      "\n",
+      " Problems or suggestions?\n",
+      "   Visit https://www.nest-simulator.org\n",
+      "\n",
+      " Type 'nest.help()' to find out more about NEST.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "from typing import List, Optional\n",
+    "\n",
+    "import matplotlib as mpl\n",
+    "\n",
+    "mpl.rcParams['axes.formatter.useoffset'] = False\n",
+    "mpl.rcParams['axes.grid'] = True\n",
+    "mpl.rcParams['grid.color'] = 'k'\n",
+    "mpl.rcParams['grid.linestyle'] = ':'\n",
+    "mpl.rcParams['grid.linewidth'] = 0.5\n",
+    "mpl.rcParams['figure.dpi'] = 120\n",
+    "mpl.rcParams['figure.figsize'] = [8., 3.]\n",
+    "\n",
+    "from collections import defaultdict\n",
+    "import copy\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import random\n",
+    "import re\n",
+    "import sys\n",
+    "import time\n",
+    "import hashlib\n",
+    "import numpy as np\n",
+    "from pathlib import Path\n",
+    "from pprint import pformat\n",
+    "from collections import Counter\n",
+    "\n",
+    "import nest\n",
+    "import nest.raster_plot\n",
+    "import parameters as para\n",
+    "\n",
+    "from pynestml.codegeneration.nest_code_generator_utils import NESTCodeGeneratorUtils\n",
+    "from pynestml.codegeneration.nest_tools import NESTTools\n",
+    "\n",
+    "n_threads = 12  # number of threads to use for simulations. This depends on your computer hardware.\n",
+    "nest_verbosity = \"M_ERROR\"  # try \"M_ALL\" for debugging"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Generating code with NESTML\n",
+    "\n",
+    "We will use a helper function to generate the C++ code for the models and build and install it as a NEST extension module. We can then load the module in the NEST kernel at runtime by calling ``nest.Install(\"nestmlmodule\")``."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "              -- N E S T --\n",
+      "  Copyright (C) 2004 The NEST Initiative\n",
+      "\n",
+      " Version: 3.8.0-post0.dev0\n",
+      " Built: Sep 26 2024 22:44:51\n",
+      "\n",
+      " This program is provided AS IS and comes with\n",
+      " NO WARRANTY. See the file LICENSE for details.\n",
+      "\n",
+      " Problems or suggestions?\n",
+      "   Visit https://www.nest-simulator.org\n",
+      "\n",
+      " Type 'nest.help()' to find out more about NEST.\n",
+      "\n",
+      "\n",
+      "Oct 16 18:53:18 NodeManager::add_node [Info]: \n",
+      "    Neuron models emitting precisely timed spikes exist: the kernel property \n",
+      "    off_grid_spiking has been set to true.\n",
+      "    \n",
+      "    NOTE: Mixing precise-spiking and normal neuron models may lead to inconsistent results.\n",
+      "[23,iaf_psc_exp_nonlineardendrite_neuron_nestml, WARNING, [76:36;76:49]]: Model contains a call to fixed-timestep functions (``resolution()`` and/or ``steps()``). This restricts the model to being compatible only with fixed-timestep simulators. Consider eliminating ``resolution()`` and ``steps()`` from the model, and using ``timestep()`` instead.\n",
+      "[24,iaf_psc_exp_nonlineardendrite_neuron_nestml, WARNING, [80:36;80:47]]: Model contains a call to fixed-timestep functions (``resolution()`` and/or ``steps()``). This restricts the model to being compatible only with fixed-timestep simulators. Consider eliminating ``resolution()`` and ``steps()`` from the model, and using ``timestep()`` instead.\n",
+      "[25,iaf_psc_exp_nonlineardendrite_neuron_nestml, WARNING, [118:31;118:42]]: Model contains a call to fixed-timestep functions (``resolution()`` and/or ``steps()``). This restricts the model to being compatible only with fixed-timestep simulators. Consider eliminating ``resolution()`` and ``steps()`` from the model, and using ``timestep()`` instead.\n",
+      "[26,iaf_psc_exp_nonlineardendrite_neuron_nestml, WARNING, [118:51;118:62]]: Model contains a call to fixed-timestep functions (``resolution()`` and/or ``steps()``). This restricts the model to being compatible only with fixed-timestep simulators. Consider eliminating ``resolution()`` and ``steps()`` from the model, and using ``timestep()`` instead.\n",
+      "[27,iaf_psc_exp_nonlineardendrite_neuron_nestml, WARNING, [119:32;119:43]]: Model contains a call to fixed-timestep functions (``resolution()`` and/or ``steps()``). This restricts the model to being compatible only with fixed-timestep simulators. Consider eliminating ``resolution()`` and ``steps()`` from the model, and using ``timestep()`` instead.\n",
+      "[28,iaf_psc_exp_nonlineardendrite_neuron_nestml, WARNING, [119:62;119:73]]: Model contains a call to fixed-timestep functions (``resolution()`` and/or ``steps()``). This restricts the model to being compatible only with fixed-timestep simulators. Consider eliminating ``resolution()`` and ``steps()`` from the model, and using ``timestep()`` instead.\n",
+      "[32,stdsp_synapse_nestml, WARNING, [20:8;20:17]]: Variable 'd' has the same name as a physical unit!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:Under certain conditions, the propagator matrix is singular (contains infinities).\n",
+      "WARNING:List of all conditions that result in a singular propagator:\n",
+      "WARNING:\ttau_m = tau_syn2\n",
+      "WARNING:\ttau_m = tau_syn3\n",
+      "WARNING:\ttau_m = tau_syn1\n",
+      "WARNING:\ttau_h = 0\n",
+      "WARNING:Not preserving expression for variable \"I_dend\" as it is solved by propagator solver\n",
+      "WARNING:Not preserving expression for variable \"I_dend__DOLLAR\" as it is solved by propagator solver\n",
+      "WARNING:Not preserving expression for variable \"V_m\" as it is solved by propagator solver\n",
+      "WARNING:Not preserving expression for variable \"dAP_trace\" as it is solved by propagator solver\n",
+      "WARNING:Under certain conditions, the propagator matrix is singular (contains infinities).\n",
+      "WARNING:List of all conditions that result in a singular propagator:\n",
+      "WARNING:\ttau_m = tau_syn2\n",
+      "WARNING:\ttau_m = tau_syn3\n",
+      "WARNING:\ttau_m = tau_syn1\n",
+      "WARNING:\ttau_h = 0\n",
+      "WARNING:Not preserving expression for variable \"I_dend\" as it is solved by propagator solver\n",
+      "WARNING:Not preserving expression for variable \"I_dend__DOLLAR\" as it is solved by propagator solver\n",
+      "WARNING:Not preserving expression for variable \"V_m\" as it is solved by propagator solver\n",
+      "WARNING:Not preserving expression for variable \"dAP_trace\" as it is solved by propagator solver\n",
+      "WARNING:Not preserving expression for variable \"pre_trace\" as it is solved by propagator solver\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[95,stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml, WARNING, [20:8;20:17]]: Variable 'd' has the same name as a physical unit!\n",
+      "[99,stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml, WARNING, [20:8;20:17]]: Variable 'd' has the same name as a physical unit!\n",
+      "CMake Warning (dev) at CMakeLists.txt:95 (project):\n",
+      "  cmake_minimum_required() should be called prior to this top-level project()\n",
+      "  call.  Please see the cmake-commands(7) manual for usage documentation of\n",
+      "  both commands.\n",
+      "This warning is for project developers.  Use -Wno-dev to suppress it.\n",
+      "\n",
+      "-- The CXX compiler identification is GNU 12.3.0\n",
+      "-- Detecting CXX compiler ABI info\n",
+      "-- Detecting CXX compiler ABI info - done\n",
+      "-- Check for working CXX compiler: /usr/bin/c++ - skipped\n",
+      "-- Detecting CXX compile features\n",
+      "-- Detecting CXX compile features - done\n",
+      "\n",
+      "-------------------------------------------------------\n",
+      "nestml_53df85df034a42159911f33aede126f7_module Configuration Summary\n",
+      "-------------------------------------------------------\n",
+      "\n",
+      "C++ compiler         : /usr/bin/c++\n",
+      "Build static libs    : OFF\n",
+      "C++ compiler flags   : \n",
+      "NEST compiler flags  :  -std=c++17 -Wall -fopenmp -O2 -fdiagnostics-color=auto\n",
+      "NEST include dirs    :  -I/home/charl/julich/nest-simulator-install/include/nest -I/usr/include -I/usr/include -I/usr/include\n",
+      "NEST libraries flags : -L/home/charl/julich/nest-simulator-install/lib/nest -lnest -lsli /usr/lib/x86_64-linux-gnu/libltdl.so  /usr/lib/x86_64-linux-gnu/libgsl.so /usr/lib/x86_64-linux-gnu/libgslcblas.so    /usr/lib/gcc/x86_64-linux-gnu/12/libgomp.so /usr/lib/x86_64-linux-gnu/libpthread.a\n",
+      "\n",
+      "-------------------------------------------------------\n",
+      "\n",
+      "You can now build and install 'nestml_53df85df034a42159911f33aede126f7_module' using\n",
+      "  make\n",
+      "  make install\n",
+      "\n",
+      "The library file libnestml_53df85df034a42159911f33aede126f7_module.so will be installed to\n",
+      "  /tmp/nestml_target_bzeptr4_\n",
+      "The module can be loaded into NEST using\n",
+      "  (nestml_53df85df034a42159911f33aede126f7_module) Install       (in SLI)\n",
+      "  nest.Install(nestml_53df85df034a42159911f33aede126f7_module)   (in PyNEST)\n",
+      "\n",
+      "CMake Warning (dev) in CMakeLists.txt:\n",
+      "  No cmake_minimum_required command is present.  A line of code such as\n",
+      "\n",
+      "    cmake_minimum_required(VERSION 3.26)\n",
+      "\n",
+      "  should be added at the top of the file.  The version specified may be lower\n",
+      "  if you wish to support older CMake versions for this project.  For more\n",
+      "  information run \"cmake --help-policy CMP0000\".\n",
+      "This warning is for project developers.  Use -Wno-dev to suppress it.\n",
+      "\n",
+      "-- Configuring done (0.1s)\n",
+      "-- Generating done (0.0s)\n",
+      "-- Build files have been written to: /home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target\n",
+      "[ 25%] Building CXX object CMakeFiles/nestml_53df85df034a42159911f33aede126f7_module_module.dir/nestml_53df85df034a42159911f33aede126f7_module.o\n",
+      "[ 50%] Building CXX object CMakeFiles/nestml_53df85df034a42159911f33aede126f7_module_module.dir/iaf_psc_exp_nonlineardendrite_neuron_nestml.o\n",
+      "[ 75%] Building CXX object CMakeFiles/nestml_53df85df034a42159911f33aede126f7_module_module.dir/iaf_psc_exp_nonlineardendrite_neuron_nestml__with_stdsp_synapse_nestml.o\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/iaf_psc_exp_nonlineardendrite_neuron_nestml.cpp: In member function ‘void iaf_psc_exp_nonlineardendrite_neuron_nestml::init_state_internal_()’:\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/iaf_psc_exp_nonlineardendrite_neuron_nestml.cpp:212:16: warning: unused variable ‘__timestep’ [-Wunused-variable]\n",
+      "  212 |   const double __timestep = nest::Time::get_resolution().get_ms();  // do not remove, this is necessary for the timestep() function\n",
+      "      |                ^~~~~~~~~~\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/iaf_psc_exp_nonlineardendrite_neuron_nestml.cpp: In member function ‘void iaf_psc_exp_nonlineardendrite_neuron_nestml::recompute_internal_variables(bool)’:\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/iaf_psc_exp_nonlineardendrite_neuron_nestml.cpp:267:16: warning: unused variable ‘__timestep’ [-Wunused-variable]\n",
+      "  267 |   const double __timestep = nest::Time::get_resolution().get_ms();  // do not remove, this is necessary for the timestep() function\n",
+      "      |                ^~~~~~~~~~\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/iaf_psc_exp_nonlineardendrite_neuron_nestml.cpp: In member function ‘virtual void iaf_psc_exp_nonlineardendrite_neuron_nestml::update(const nest::Time&, long int, long int)’:\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/iaf_psc_exp_nonlineardendrite_neuron_nestml.cpp:339:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n",
+      "  339 |     for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n",
+      "      |                      ~~^~~~~~~~~~~~~~~~~~~~~\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/iaf_psc_exp_nonlineardendrite_neuron_nestml.cpp:330:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n",
+      "  330 |     auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n",
+      "      |          ^~~~~\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/iaf_psc_exp_nonlineardendrite_neuron_nestml.cpp:324:16: warning: unused variable ‘__timestep’ [-Wunused-variable]\n",
+      "  324 |   const double __timestep = nest::Time::get_resolution().get_ms();  // do not remove, this is necessary for the timestep() function\n",
+      "      |                ^~~~~~~~~~\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/iaf_psc_exp_nonlineardendrite_neuron_nestml__with_stdsp_synapse_nestml.cpp: In member function ‘void iaf_psc_exp_nonlineardendrite_neuron_nestml__with_stdsp_synapse_nestml::init_state_internal_()’:\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/iaf_psc_exp_nonlineardendrite_neuron_nestml__with_stdsp_synapse_nestml.cpp:217:16: warning: unused variable ‘__timestep’ [-Wunused-variable]\n",
+      "  217 |   const double __timestep = nest::Time::get_resolution().get_ms();  // do not remove, this is necessary for the timestep() function\n",
+      "      |                ^~~~~~~~~~\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/iaf_psc_exp_nonlineardendrite_neuron_nestml.cpp: In member function ‘void iaf_psc_exp_nonlineardendrite_neuron_nestml::on_receive_block_I_2()’:\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/iaf_psc_exp_nonlineardendrite_neuron_nestml.cpp:523:16: warning: unused variable ‘__timestep’ [-Wunused-variable]\n",
+      "  523 |   const double __timestep = nest::Time::get_resolution().get_ms();  // do not remove, this is necessary for the timestep() function\n",
+      "      |                ^~~~~~~~~~\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/iaf_psc_exp_nonlineardendrite_neuron_nestml__with_stdsp_synapse_nestml.cpp: In member function ‘void iaf_psc_exp_nonlineardendrite_neuron_nestml__with_stdsp_synapse_nestml::recompute_internal_variables(bool)’:\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/iaf_psc_exp_nonlineardendrite_neuron_nestml__with_stdsp_synapse_nestml.cpp:278:16: warning: unused variable ‘__timestep’ [-Wunused-variable]\n",
+      "  278 |   const double __timestep = nest::Time::get_resolution().get_ms();  // do not remove, this is necessary for the timestep() function\n",
+      "      |                ^~~~~~~~~~\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/iaf_psc_exp_nonlineardendrite_neuron_nestml__with_stdsp_synapse_nestml.cpp: In member function ‘virtual void iaf_psc_exp_nonlineardendrite_neuron_nestml__with_stdsp_synapse_nestml::update(const nest::Time&, long int, long int)’:\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/iaf_psc_exp_nonlineardendrite_neuron_nestml__with_stdsp_synapse_nestml.cpp:350:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n",
+      "  350 |     for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n",
+      "      |                      ~~^~~~~~~~~~~~~~~~~~~~~\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/iaf_psc_exp_nonlineardendrite_neuron_nestml__with_stdsp_synapse_nestml.cpp:341:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n",
+      "  341 |     auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n",
+      "      |          ^~~~~\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/iaf_psc_exp_nonlineardendrite_neuron_nestml__with_stdsp_synapse_nestml.cpp:335:16: warning: unused variable ‘__timestep’ [-Wunused-variable]\n",
+      "  335 |   const double __timestep = nest::Time::get_resolution().get_ms();  // do not remove, this is necessary for the timestep() function\n",
+      "      |                ^~~~~~~~~~\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/iaf_psc_exp_nonlineardendrite_neuron_nestml__with_stdsp_synapse_nestml.cpp: In member function ‘void iaf_psc_exp_nonlineardendrite_neuron_nestml__with_stdsp_synapse_nestml::on_receive_block_I_2()’:\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/iaf_psc_exp_nonlineardendrite_neuron_nestml__with_stdsp_synapse_nestml.cpp:535:16: warning: unused variable ‘__timestep’ [-Wunused-variable]\n",
+      "  535 |   const double __timestep = nest::Time::get_resolution().get_ms();  // do not remove, this is necessary for the timestep() function\n",
+      "      |                ^~~~~~~~~~\n",
+      "In file included from /home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/nestml_53df85df034a42159911f33aede126f7_module.cpp:36:\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml.h: In instantiation of ‘nest::stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml<targetidentifierT>::stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n",
+      "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:164:25:   required from ‘nest::GenericConnectorModel<ConnectionT>::GenericConnectorModel(std::string) [with ConnectionT = nest::stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml<nest::TargetIdentifierPtrRport>; std::string = std::__cxx11::basic_string<char>]’\n",
+      "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:62:5:   required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml; std::string = std::__cxx11::basic_string<char>]’\n",
+      "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:37:70:   required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml; std::string = std::__cxx11::basic_string<char>]’\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml.h:694:108:   required from here\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml.h:842:16: warning: unused variable ‘__timestep’ [-Wunused-variable]\n",
+      "  842 |   const double __timestep = nest::Time::get_resolution().get_ms();  // do not remove, this is necessary for the timestep() function\n",
+      "      |                ^~~~~~~~~~\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml.h: In instantiation of ‘void nest::stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml<targetidentifierT>::recompute_internal_variables() [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml.h:859:3:   required from ‘nest::stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml<targetidentifierT>::stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierPtrRport]’\n",
+      "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:164:25:   required from ‘nest::GenericConnectorModel<ConnectionT>::GenericConnectorModel(std::string) [with ConnectionT = nest::stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml<nest::TargetIdentifierPtrRport>; std::string = std::__cxx11::basic_string<char>]’\n",
+      "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:62:5:   required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml; std::string = std::__cxx11::basic_string<char>]’\n",
+      "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:37:70:   required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml; std::string = std::__cxx11::basic_string<char>]’\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml.h:694:108:   required from here\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml.h:830:16: warning: unused variable ‘__timestep’ [-Wunused-variable]\n",
+      "  830 |   const double __timestep = nest::Time::get_resolution().get_ms();  // do not remove, this is necessary for the timestep() function\n",
+      "      |                ^~~~~~~~~~\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml.h: In instantiation of ‘nest::stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml<targetidentifierT>::stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierIndex]’:\n",
+      "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:164:25:   required from ‘nest::GenericConnectorModel<ConnectionT>::GenericConnectorModel(std::string) [with ConnectionT = nest::stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml<nest::TargetIdentifierIndex>; std::string = std::__cxx11::basic_string<char>]’\n",
+      "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:103:34:   required from ‘void nest::ModelManager::register_specific_connection_model_(const std::string&) [with CompleteConnecionT = nest::stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml<nest::TargetIdentifierIndex>; std::string = std::__cxx11::basic_string<char>]’\n",
+      "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:67:80:   required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml; std::string = std::__cxx11::basic_string<char>]’\n",
+      "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:37:70:   required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml; std::string = std::__cxx11::basic_string<char>]’\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml.h:694:108:   required from here\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml.h:842:16: warning: unused variable ‘__timestep’ [-Wunused-variable]\n",
+      "  842 |   const double __timestep = nest::Time::get_resolution().get_ms();  // do not remove, this is necessary for the timestep() function\n",
+      "      |                ^~~~~~~~~~\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml.h: In instantiation of ‘void nest::stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml<targetidentifierT>::recompute_internal_variables() [with targetidentifierT = nest::TargetIdentifierIndex]’:\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml.h:859:3:   required from ‘nest::stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml<targetidentifierT>::stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierIndex]’\n",
+      "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:164:25:   required from ‘nest::GenericConnectorModel<ConnectionT>::GenericConnectorModel(std::string) [with ConnectionT = nest::stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml<nest::TargetIdentifierIndex>; std::string = std::__cxx11::basic_string<char>]’\n",
+      "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:103:34:   required from ‘void nest::ModelManager::register_specific_connection_model_(const std::string&) [with CompleteConnecionT = nest::stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml<nest::TargetIdentifierIndex>; std::string = std::__cxx11::basic_string<char>]’\n",
+      "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:67:80:   required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml; std::string = std::__cxx11::basic_string<char>]’\n",
+      "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:37:70:   required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml; std::string = std::__cxx11::basic_string<char>]’\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml.h:694:108:   required from here\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml.h:830:16: warning: unused variable ‘__timestep’ [-Wunused-variable]\n",
+      "  830 |   const double __timestep = nest::Time::get_resolution().get_ms();  // do not remove, this is necessary for the timestep() function\n",
+      "      |                ^~~~~~~~~~\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml.h: In instantiation of ‘bool nest::stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml<targetidentifierT>::send(nest::Event&, size_t, const nest::stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport; size_t = long unsigned int]’:\n",
+      "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22:   required from ‘void nest::Connector<ConnectionT>::send_to_all(size_t, const std::vector<nest::ConnectorModel*>&, nest::Event&) [with ConnectionT = nest::stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml<nest::TargetIdentifierPtrRport>; size_t = long unsigned int]’\n",
+      "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3:   required from here\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml.h:607:22: warning: unused variable ‘__dAP_trace’ [-Wunused-variable]\n",
+      "  607 |         const double __dAP_trace = ((post_neuron_t*)(__target))->get_dAP_trace();\n",
+      "      |                      ^~~~~~~~~~~\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml.h:487:18: warning: unused variable ‘__timestep’ [-Wunused-variable]\n",
+      "  487 |     const double __timestep = nest::Time::get_resolution().get_ms();  // do not remove, this is necessary for the timestep() function\n",
+      "      |                  ^~~~~~~~~~\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml.h:489:10: warning: variable ‘get_thread’ set but not used [-Wunused-but-set-variable]\n",
+      "  489 |     auto get_thread = [tid]()\n",
+      "      |          ^~~~~~~~~~\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml.h:598:18: warning: unused variable ‘_tr_t’ [-Wunused-variable]\n",
+      "  598 |     const double _tr_t = __t_spike - __dendritic_delay;\n",
+      "      |                  ^~~~~\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml.h: In instantiation of ‘bool nest::stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml<targetidentifierT>::send(nest::Event&, size_t, const nest::stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex; size_t = long unsigned int]’:\n",
+      "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22:   required from ‘void nest::Connector<ConnectionT>::send_to_all(size_t, const std::vector<nest::ConnectorModel*>&, nest::Event&) [with ConnectionT = nest::stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml<nest::TargetIdentifierIndex>; size_t = long unsigned int]’\n",
+      "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3:   required from here\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml.h:607:22: warning: unused variable ‘__dAP_trace’ [-Wunused-variable]\n",
+      "  607 |         const double __dAP_trace = ((post_neuron_t*)(__target))->get_dAP_trace();\n",
+      "      |                      ^~~~~~~~~~~\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml.h:487:18: warning: unused variable ‘__timestep’ [-Wunused-variable]\n",
+      "  487 |     const double __timestep = nest::Time::get_resolution().get_ms();  // do not remove, this is necessary for the timestep() function\n",
+      "      |                  ^~~~~~~~~~\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml.h:489:10: warning: variable ‘get_thread’ set but not used [-Wunused-but-set-variable]\n",
+      "  489 |     auto get_thread = [tid]()\n",
+      "      |          ^~~~~~~~~~\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml.h:598:18: warning: unused variable ‘_tr_t’ [-Wunused-variable]\n",
+      "  598 |     const double _tr_t = __t_spike - __dendritic_delay;\n",
+      "      |                  ^~~~~\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml.h: In instantiation of ‘void nest::stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml<targetidentifierT>::update_internal_state_(double, double, const nest::stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml.h:561:9:   required from ‘bool nest::stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml<targetidentifierT>::send(nest::Event&, size_t, const nest::stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport; size_t = long unsigned int]’\n",
+      "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22:   required from ‘void nest::Connector<ConnectionT>::send_to_all(size_t, const std::vector<nest::ConnectorModel*>&, nest::Event&) [with ConnectionT = nest::stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml<nest::TargetIdentifierPtrRport>; size_t = long unsigned int]’\n",
+      "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3:   required from here\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml.h:914:18: warning: unused variable ‘__timestep’ [-Wunused-variable]\n",
+      "  914 |     const double __timestep = timestep;  // do not remove, this is necessary for the timestep() function\n",
+      "      |                  ^~~~~~~~~~\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml.h:915:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n",
+      "  915 |     auto get_t = [t_start](){ return t_start; };   // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n",
+      "      |          ^~~~~\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml.h: In instantiation of ‘void nest::stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml<targetidentifierT>::update_internal_state_(double, double, const nest::stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex]’:\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml.h:561:9:   required from ‘bool nest::stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml<targetidentifierT>::send(nest::Event&, size_t, const nest::stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex; size_t = long unsigned int]’\n",
+      "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22:   required from ‘void nest::Connector<ConnectionT>::send_to_all(size_t, const std::vector<nest::ConnectorModel*>&, nest::Event&) [with ConnectionT = nest::stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml<nest::TargetIdentifierIndex>; size_t = long unsigned int]’\n",
+      "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3:   required from here\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml.h:914:18: warning: unused variable ‘__timestep’ [-Wunused-variable]\n",
+      "  914 |     const double __timestep = timestep;  // do not remove, this is necessary for the timestep() function\n",
+      "      |                  ^~~~~~~~~~\n",
+      "/home/charl/julich/nestml-fork-sequence-learning/nestml/doc/tutorials/sequence_learning/target/stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml.h:915:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n",
+      "  915 |     auto get_t = [t_start](){ return t_start; };   // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n",
+      "      |          ^~~~~\n",
+      "[100%] Linking CXX shared module nestml_53df85df034a42159911f33aede126f7_module.so\n",
+      "[100%] Built target nestml_53df85df034a42159911f33aede126f7_module_module\n",
+      "[100%] Built target nestml_53df85df034a42159911f33aede126f7_module_module\n",
+      "Install the project...\n",
+      "-- Install configuration: \"\"\n",
+      "-- Installing: /tmp/nestml_target_bzeptr4_/nestml_53df85df034a42159911f33aede126f7_module.so\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'module_name = \"/tmp/nestml_target_68digoes/nestml_27fd8d9de14b408f9e6181b2f2310d41_module.so\"\\nneuron_model_name = \"iaf_psc_exp_nonlineardendrite_neuron_nestml__with_stdsp_synapse_nestml\"\\nsynapse_model_name = \"stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml\" '"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "if 1:\n",
+    "    try:\n",
+    "        module_name, neuron_model_name, synapse_model_name = \\\n",
+    "            NESTCodeGeneratorUtils.generate_code_for(\"doc/tutorials/sequence_learning/iaf_psc_exp_nonlineardendrite_neuron.nestml\",\n",
+    "                                                     \"models/synapses/stdsp_synapse.nestml\",\n",
+    "                                                     logging_level=\"WARNING\",\n",
+    "                                                     post_ports=[\"post_spikes\", [\"dAP_trace\", \"dAP_trace\"]],\n",
+    "                                                     codegen_opts={\"delay_variable\": {\"stdsp_synapse\": \"d\"},\n",
+    "                                                                   \"weight_variable\": {\"stdsp_synapse\": \"w\"},\n",
+    "                                                                   \"continuous_state_buffering_method\": \"post_spike_based\"})\n",
+    "    except:\n",
+    "            \n",
+    "        module_name, neuron_model_name, synapse_model_name = \\\n",
+    "            NESTCodeGeneratorUtils.generate_code_for(\"../../../doc/tutorials/sequence_learning/iaf_psc_exp_nonlineardendrite_neuron.nestml\",\n",
+    "                                                     \"../../../models/synapses/stdsp_synapse.nestml\",\n",
+    "                                                     logging_level=\"WARNING\",\n",
+    "                                                     post_ports=[\"post_spikes\", [\"dAP_trace\", \"dAP_trace\"]],\n",
+    "                                                     codegen_opts={\"delay_variable\": {\"stdsp_synapse\": \"d\"},\n",
+    "                                                                   \"weight_variable\": {\"stdsp_synapse\": \"w\"},\n",
+    "                                                                   \"continuous_state_buffering_method\": \"post_spike_based\"})\n",
+    "\"\"\"module_name = \"/tmp/nestml_target_68digoes/nestml_27fd8d9de14b408f9e6181b2f2310d41_module.so\"\n",
+    "neuron_model_name = \"iaf_psc_exp_nonlineardendrite_neuron_nestml__with_stdsp_synapse_nestml\"\n",
+    "synapse_model_name = \"stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml\" \"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, the NESTML models are ready to be used in a simulation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Experiment 1: dAP generation in the neuron model\n",
+    "\n",
+    "First, let's inspect the generation of a dendritic action potential in a single neuron."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "code_folding": [],
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def psp_max_2_psc_max(psp_max, tau_m, tau_s, R_m):\n",
+    "    \"\"\"Compute the PSC amplitude (pA) injected to get a certain PSP maximum (mV) for LIF with exponential PSCs\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    psp_max: float\n",
+    "             Maximum postsynaptic pontential\n",
+    "    tau_m:   float\n",
+    "             Membrane time constant (ms).\n",
+    "    tau_s:   float\n",
+    "             Synaptic time constant (ms).\n",
+    "    R_m:     float\n",
+    "             Membrane resistance (Gohm).\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    float\n",
+    "        PSC amplitude (pA).\n",
+    "    \"\"\"\n",
+    "\n",
+    "    return psp_max / (\n",
+    "            R_m * tau_s / (tau_s - tau_m) * (\n",
+    "            (tau_m / tau_s) ** (-tau_m / (tau_m - tau_s)) -\n",
+    "            (tau_m / tau_s) ** (-tau_s / (tau_m - tau_s))\n",
+    "    )\n",
+    "    )\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "code_folding": [
+     0
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "def create_active_dendrite_parameters():\n",
+    "    p = para.ParameterSpace({})\n",
+    "    \n",
+    "    DELAY = 0.1\n",
+    "    \n",
+    "    p['dt'] = 0.1                                 # simulation time resolution (ms)\n",
+    "    p['print_simulation_progress'] = False        # print the time progress -- True can cause issues with Jupyter\n",
+    "    \n",
+    "    # neuron parameters of the excitatory neurons\n",
+    "    p['soma_model'] = neuron_model_name\n",
+    "    p['soma_params'] = {}\n",
+    "    p['soma_params']['C_m'] = 250.        # membrane capacitance (pF)\n",
+    "    p['soma_params']['E_L'] = 0.          # resting membrane potential (mV)\n",
+    "    p['soma_params']['I_e'] = 0.          # external DC currents (pA)\n",
+    "    p['soma_params']['V_m'] = 0.          # initial potential (mV)\n",
+    "    p['soma_params']['V_reset'] = 0.      # reset potential (mV)\n",
+    "    p['soma_params']['V_th'] = 20.        # spike threshold (mV)\n",
+    "    p['soma_params']['t_ref'] = 10.       # refractory period\n",
+    "    p['soma_params']['tau_m'] = 10.       # membrane time constant (ms)\n",
+    "    p['soma_params']['tau_syn1'] = 2.     # synaptic time constant: external input (receptor 1)\n",
+    "    p['soma_params']['tau_syn2'] = 5.     # synaptic time constant: dendrtic input (receptor 2)\n",
+    "    p['soma_params']['tau_syn3'] = 1.     # synaptic time constant: inhibitory input (receptor 3)\n",
+    "    \n",
+    "    # dendritic action potential\n",
+    "    p['soma_params']['I_p'] = 200. # current clamp value for I_dAP during a dendritic action potenti\n",
+    "    p['soma_params']['tau_dAP'] = 60.       # time window over which the dendritic current clamp is active\n",
+    "    p['soma_params']['theta_dAP'] = 59.        # current threshold for a dendritic action potential\n",
+    "    \n",
+    "    p['soma_params']['I_dend_incr'] = 2.71 / (p['soma_params']['tau_syn2'])\n",
+    "    \n",
+    "    p['fixed_somatic_delay'] = 2          # this is an approximate time of how long it takes the soma to fire\n",
+    "                                          # upon receiving an external stimulus \n",
+    "    \n",
+    "    # neuron parameters for the inhibitory neuron\n",
+    "    p['inhibit_model'] = 'iaf_psc_exp'\n",
+    "    p['inhibit_params'] = {}\n",
+    "    p['inhibit_params']['C_m'] = 250.         # membrane capacitance (pF)\n",
+    "    p['inhibit_params']['E_L'] = 0.           # resting membrane potential (mV)\n",
+    "    p['inhibit_params']['I_e'] = 0.           # external DC currents (pA)\n",
+    "    p['inhibit_params']['V_m'] = 0.           # initial potential (mV)\n",
+    "    p['inhibit_params']['V_reset'] = 0.       # reset potential (mV)\n",
+    "    p['inhibit_params']['V_th'] = 15.         # spike threshold (mV)\n",
+    "    p['inhibit_params']['t_ref'] = 2.0        # refractory period\n",
+    "    p['inhibit_params']['tau_m'] = 5.         # membrane time constant (ms)\n",
+    "    p['inhibit_params']['tau_syn_ex'] = .5    # synaptic time constant of an excitatory input (ms) \n",
+    "    p['inhibit_params']['tau_syn_in'] = 1.65  # synaptic time constant of an inhibitory input (ms)\n",
+    "    \n",
+    "    # synaptic parameters\n",
+    "    p['J_EX_psp'] = 1.1 * p['soma_params']['V_th']     # somatic PSP as a response to an external input\n",
+    "    p['J_IE_psp'] = 1.2 * p['inhibit_params']['V_th']  # inhibitory PSP as a response to an input from E neuron\n",
+    "    p['J_EI_psp'] = -2 * p['soma_params']['V_th']      # somatic PSP as a response to an inhibitory input\n",
+    "    p['convergence'] = 5\n",
+    "    p['pattern_size'] = 20\n",
+    "    \n",
+    "    # parameters for ee synapses (stdsp)\n",
+    "    p['syn_dict_ee'] = {}\n",
+    "    p['syn_dict_ee']['weight'] = 0.01                    # synaptic weight\n",
+    "    p['syn_dict_ee']['synapse_model'] = synapse_model_name  # synapse model\n",
+    "    p['syn_dict_ee']['permanence_threshold'] = 10.                    # synapse maturity threshold\n",
+    "    p['syn_dict_ee']['tau_pre_trace'] = 20.                   # plasticity time constant (potentiation)\n",
+    "    p['syn_dict_ee']['delay'] = 2.                       # dendritic delay \n",
+    "    p['syn_dict_ee']['receptor_type'] = 2                # receptor corresponding to the dendritic input\n",
+    "    p['syn_dict_ee']['lambda_plus'] = 0.05 #0.1                     # potentiation rate\n",
+    "    p['syn_dict_ee']['zt'] = 1.                          # target dAP trace [pA]\n",
+    "    p['syn_dict_ee']['lambda_h'] = 0.01                        # homeostasis rate\n",
+    "    p['syn_dict_ee']['Wmax'] = 1.1 * p['soma_params']['theta_dAP'] / p['convergence']   # Maximum allowed weight\n",
+    "    p['syn_dict_ee']['permanence_max'] = 20.                       # Maximum allowed permanence\n",
+    "    p['syn_dict_ee']['permanence_min'] = 1.                        # Minimum allowed permanence\n",
+    "    p['syn_dict_ee']['lambda_minus'] = 0.004\n",
+    "    \n",
+    "    # parameters of EX synapses (external to soma of E neurons)\n",
+    "    p['conn_dict_ex'] = {}\n",
+    "    p['syn_dict_ex'] = {}\n",
+    "    p['syn_dict_ex']['receptor_type'] = 1                    # receptor corresponding to external input\n",
+    "    p['syn_dict_ex']['delay'] = DELAY                        # dendritic delay\n",
+    "    p['conn_dict_ex']['rule'] = 'all_to_all'                 # connection rule\n",
+    "    \n",
+    "    # parameters of EdX synapses (external to dendrite of E neurons) \n",
+    "    p['conn_dict_edx'] = {}\n",
+    "    p['syn_dict_edx'] = {}\n",
+    "    p['syn_dict_edx']['receptor_type'] = 2                    # receptor corresponding to the dendritic input\n",
+    "    p['syn_dict_edx']['delay'] = DELAY                        # dendritic delay\n",
+    "    p['syn_dict_edx']['weight'] = 1.4 * p['soma_params']['theta_dAP']\n",
+    "    p['conn_dict_edx']['rule'] = 'fixed_outdegree'            # connection rule\n",
+    "    p['conn_dict_edx']['outdegree'] = p['pattern_size'] + 1   # outdegree\n",
+    "    \n",
+    "    # parameters for IE synapses \n",
+    "    p['syn_dict_ie'] = {}\n",
+    "    p['syn_dict_ie']['synapse_model'] = 'static_synapse'     # synapse model\n",
+    "    p['syn_dict_ie']['delay'] = DELAY                        # dendritic delay\n",
+    "    \n",
+    "    # parameters for EI synapses\n",
+    "    p['syn_dict_ei'] = {}\n",
+    "    p['syn_dict_ei']['synapse_model'] = 'static_synapse'     # synapse model\n",
+    "    p['syn_dict_ei']['delay'] = DELAY                        # dendritic delay\n",
+    "    p['syn_dict_ei']['receptor_type'] = 3                    # receptor corresponding to the inhibitory input  \n",
+    "    \n",
+    "    p['R_m_soma'] = p['soma_params']['tau_m'] / p['soma_params']['C_m']\n",
+    "    p['R_m_inhibit'] = p['inhibit_params']['tau_m'] / p['inhibit_params']['C_m']\n",
+    "    p['syn_dict_ex']['weight'] = psp_max_2_psc_max(p['J_EX_psp'], \n",
+    "                                                               p['soma_params']['tau_m'], \n",
+    "                                                               p['soma_params']['tau_syn1'], \n",
+    "                                                               p['R_m_soma'])\n",
+    "    p['syn_dict_ie']['weight'] = psp_max_2_psc_max(p['J_IE_psp'], \n",
+    "                                                               p['inhibit_params']['tau_m'], \n",
+    "                                                               p['inhibit_params']['tau_syn_ex'], \n",
+    "                                                               p['R_m_inhibit'])\n",
+    "    p['syn_dict_ei']['weight'] = psp_max_2_psc_max(p['J_EI_psp'], \n",
+    "                                                               p['soma_params']['tau_m'], \n",
+    "                                                               p['soma_params']['tau_syn3'], \n",
+    "                                                               p['R_m_soma'])\n",
+    "\n",
+    "    \n",
+    "    p['soma_excitation_time'] = 25.\n",
+    "    p['dendrite_excitation_time'] = 3.\n",
+    "\n",
+    "    return p\n",
+    "\n",
+    "p = create_active_dendrite_parameters()"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": "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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The network looks as follows:\n",
+    "\n",
+    "![image.png](attachment:image.png)\n",
+    "\n",
+    "The postsynaptic potentials look as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "code_folding": [
+     0
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running active dendrite simulation!\n",
+      "Running experiment type: ff\n",
+      "\n",
+      "Oct 16 18:53:40 Install [Info]: \n",
+      "    loaded module nestml_53df85df034a42159911f33aede126f7_module\n",
+      "### simulating network\n",
+      "Running experiment type: dendrite\n",
+      "### simulating network\n",
+      "Running experiment type: ff_dendrite\n",
+      "### simulating network\n"
+     ]
+    }
+   ],
+   "source": [
+    "def run_active_dendrite_simulation(params):\n",
+    "    print(\"Running active dendrite simulation!\")\n",
+    "    data = {}\n",
+    "    for i, name in enumerate(['ff', 'dendrite', 'ff_dendrite']): \n",
+    "        print(\"Running experiment type: \" + name)\n",
+    "        # init kernel\n",
+    "        seed = 1\n",
+    "        nest.ResetKernel()\n",
+    "        nest.Install(module_name)\n",
+    "        nest.set_verbosity(nest_verbosity)\n",
+    "        nest.SetKernelStatus({\n",
+    "            'resolution': params['dt'],\n",
+    "            'print_time': params['print_simulation_progress'],\n",
+    "            'local_num_threads': n_threads,\n",
+    "            'rng_seed': seed\n",
+    "        })\n",
+    "    \n",
+    "        data[name] = {}\n",
+    "    \n",
+    "        #############################\n",
+    "        # create and connect neurons\n",
+    "        # ---------------------------\n",
+    "    \n",
+    "        # create excitatory population\n",
+    "        exc_neuron = nest.Create(params['soma_model'], params=params['soma_params'])\n",
+    "    \n",
+    "        # create inhibitory population\n",
+    "        inh_neuron = nest.Create(params['inhibit_model'], params=params['inhibit_params'])\n",
+    "    \n",
+    "        # connect inhibition\n",
+    "        nest.Connect(exc_neuron, inh_neuron, syn_spec=params['syn_dict_ie'])\n",
+    "        nest.Connect(inh_neuron, exc_neuron, syn_spec=params['syn_dict_ei'])\n",
+    "    \n",
+    "        ######################\n",
+    "        # Input stream/stimuli\n",
+    "        #---------------------\n",
+    "        input_excitation = nest.Create('spike_generator', params={'spike_times': [params['soma_excitation_time']]})\n",
+    "        dendrite_excitation_1 = nest.Create('spike_generator', params={'spike_times': [params['dendrite_excitation_time']]})\n",
+    "        inhibition_excitation = nest.Create('spike_generator', params={'spike_times': [10.]})\n",
+    "    \n",
+    "        # excitation soma feedforward\n",
+    "        if name == 'ff' or name == 'ff_dendrite':\n",
+    "            nest.Connect(input_excitation, exc_neuron, syn_spec={'receptor_type': 1, \n",
+    "                                                                 'weight': params['syn_dict_ex']['weight'], \n",
+    "                                                                 'delay': params['syn_dict_ex']['delay']})\n",
+    "\n",
+    "        # excitation dendrite \n",
+    "        if name == 'dendrite' or name == 'ff_dendrite':\n",
+    "            nest.Connect(dendrite_excitation_1, exc_neuron, syn_spec={'receptor_type': 2, \n",
+    "                                                                      'weight': params['syn_dict_edx']['weight'], \n",
+    "                                                                      'delay': params['syn_dict_edx']['delay']})\n",
+    "    \n",
+    "        # record voltage inhibitory neuron \n",
+    "        vm_inh = nest.Create('voltmeter', params={'record_from': ['V_m'], 'interval': 0.1})\n",
+    "        nest.Connect(vm_inh, inh_neuron)\n",
+    "    \n",
+    "        # record voltage soma\n",
+    "        vm_exc = nest.Create('voltmeter', params={'record_from': ['V_m'], 'interval': 0.1})\n",
+    "        nest.Connect(vm_exc, exc_neuron)\n",
+    "    \n",
+    "        active_dendrite_exc_mm = nest.Create('multimeter', params={'record_from': ['active_dendrite_readout', 'I_dend'], 'interval': 0.1})\n",
+    "        nest.Connect(active_dendrite_exc_mm, exc_neuron)\n",
+    "    \n",
+    "        # record spikes\n",
+    "        sd = nest.Create('spike_recorder')\n",
+    "        nest.Connect(exc_neuron, sd)\n",
+    "    \n",
+    "        # record inh spikes\n",
+    "        sd_inh = nest.Create('spike_recorder')\n",
+    "        nest.Connect(inh_neuron, sd_inh)\n",
+    "    \n",
+    "        print('### simulating network')\n",
+    "        nest.Prepare()\n",
+    "        nest.Run(100.)\n",
+    "    \n",
+    "        voltage_soma = nest.GetStatus(vm_exc)[0]['events']  \n",
+    "        active_dendrite = nest.GetStatus(active_dendrite_exc_mm)[0]['events']\n",
+    "        voltage_inhibit = nest.GetStatus(vm_inh)[0]['events'] \n",
+    "        spikes_soma = nest.GetStatus(sd)[0]['events'] \n",
+    "        spikes_inh = nest.GetStatus(sd_inh)[0]['events'] \n",
+    "    \n",
+    "        data[name]['exc'] = voltage_soma \n",
+    "        data[name]['exc_active_dendrite'] = active_dendrite \n",
+    "        data[name]['inh'] = voltage_inhibit\n",
+    "        data[name]['spikes_exc'] = spikes_soma\n",
+    "        data[name]['spikes_inh'] = spikes_inh\n",
+    "\n",
+    "    return data\n",
+    "\n",
+    "data = run_active_dendrite_simulation(p)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 960x360 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_active_dendrite_simulation(params, data):\n",
+    "    \n",
+    "    def position_excitation_arrows(ax, soma_time, dendrite_time):\n",
+    "    \n",
+    "        arrow_width = 1.8\n",
+    "        arrow_height = 1.8\n",
+    "        y = -2.3\n",
+    "        \n",
+    "        # plot excitation arrows for panel A\n",
+    "        x = soma_time - arrow_width/2 \n",
+    "        pos = [x, y]\n",
+    "        X = np.array([pos, [pos[0]+arrow_width, pos[1]], [pos[0]+arrow_width/2, pos[1]+arrow_height]])\n",
+    "        t1 = plt.Polygon(X, color=color_somatic_input)\n",
+    "        ax[0].add_patch(t1)\n",
+    "    \n",
+    "        # plot excitation arrows for panel B\n",
+    "        x = dendrite_time - arrow_width/2 \n",
+    "        pos = [x, y]\n",
+    "        X = np.array([pos, [pos[0]+arrow_width, pos[1]], [pos[0]+arrow_width/2, pos[1]+arrow_height]])\n",
+    "        t1 = plt.Polygon(X, color=color_dAP_input)\n",
+    "        ax[1].add_patch(t1)\n",
+    "    \n",
+    "        # plot excitation arrows for panel C\n",
+    "        x = dendrite_time - arrow_width/2 \n",
+    "        pos = [x, y]\n",
+    "        X = np.array([pos, [pos[0]+arrow_width, pos[1]], [pos[0]+arrow_width/2, pos[1]+arrow_height]])\n",
+    "        t1 = plt.Polygon(X, color=color_dAP_input)\n",
+    "        ax[2].add_patch(t1)\n",
+    "    \n",
+    "        x = soma_time - arrow_width/2 \n",
+    "        pos = [x, y]\n",
+    "        X = np.array([pos, [pos[0]+arrow_width, pos[1]], [pos[0]+arrow_width/2, pos[1]+arrow_height]])\n",
+    "        t1 = plt.Polygon(X, color=color_somatic_input)\n",
+    "        ax[2].add_patch(t1)\n",
+    "    \n",
+    "    \n",
+    "    color_dAP_input = '#8e7c42ff'\n",
+    "    #color_somatic_input = '#0000ffff'\n",
+    "    color_somatic_input = '#4581a7ff'\n",
+    "    color_soma = '#000000ff'\n",
+    "    color_dAP = '#00B4BE' \n",
+    "    color_inhibit = '#808080ff'  \n",
+    "    color_hrl = 'black'\n",
+    "    \n",
+    "    #color_somatic_spike = '#ff0000ff'\n",
+    "    color_somatic_spike = color_soma\n",
+    "    color_inh_spike = color_inhibit\n",
+    "    ms_spike = 7\n",
+    "    mew_spike = 1.5\n",
+    "    lw_vtheta = 0.5\n",
+    "    lw_dAP = 1.5\n",
+    "    lw_s = 1.5\n",
+    "    lw_i = 1.5\n",
+    "    \n",
+    "    # plot settings \n",
+    "    fig_size = (6., 5)\n",
+    "    ymin = -4\n",
+    "    ymax = params['soma_params']['V_th'] + 4\n",
+    "    xmin = 0  \n",
+    "    xmax = 85\n",
+    "    label_pos = (-0.18, 1.)\n",
+    "    panel_labels = ['A', 'B', 'C']\n",
+    "    v_th=params['soma_params']['V_th'] \n",
+    "    time_dAP = 10\n",
+    "    \n",
+    "    # set up the figure frame\n",
+    "    fig = plt.figure()\n",
+    "    gs = mpl.gridspec.GridSpec(5, 1, height_ratios=[15,15,15,5,6], bottom=0.1, right=0.95, top=0.93, wspace=0., hspace=0.1)\n",
+    "    left, bottom, width, height = [0.4, 0.1, 0.2, 0.2]\n",
+    "    axes = []\n",
+    "    \n",
+    "    for i, name in enumerate(['ff', 'dendrite', 'ff_dendrite']):\n",
+    "        ax = plt.subplot(gs[i,0])\n",
+    "        ax.text(label_pos[0], label_pos[1], panel_labels[i], transform=ax.transAxes, horizontalalignment='center', verticalalignment='center', size=10, weight='bold')\n",
+    "        ax.plot(data[name]['exc']['times'], data[name]['exc']['V_m'], lw=lw_s, color=color_soma, zorder=2, label='excitatory neuron')  \n",
+    "        ax.plot(data[name]['exc_active_dendrite']['times'], data[name]['exc_active_dendrite']['active_dendrite_readout'], lw=lw_s, color=color_dAP)  \n",
+    "        \n",
+    "        ax_ = ax.twinx()\n",
+    "        ax_.plot(data[name]['exc_active_dendrite']['times'], data[name]['exc_active_dendrite']['I_dend'], lw=lw_s, color=\"red\", label=\"I_dend\")  \n",
+    "        ax_.plot((0., np.amax(data[name]['exc_active_dendrite']['times'])), 2*[p['soma_params']['theta_dAP']], c=\"red\", linestyle=':')\n",
+    "                 \n",
+    "        ax.plot(data[name]['spikes_exc']['times'], (v_th+2)*np.ones(len(data[name]['spikes_exc']['times'])), '|', c=color_somatic_spike, ms=ms_spike, mew=mew_spike)\n",
+    "        ax.plot(data[name]['spikes_inh']['times'], (v_th+2)*np.ones(len(data[name]['spikes_inh']['times'])), '|', c=color_inh_spike, ms=ms_spike, mew=mew_spike)\n",
+    "        ax.legend()\n",
+    "     \n",
+    "        # add dendritic action potential bar manually\n",
+    "        if name == 'dendrite': \n",
+    "            ax.hlines(v_th+2, time_dAP, time_dAP+params['soma_params']['tau_dAP'], lw=lw_dAP, color=color_dAP)\n",
+    "    \n",
+    "        if name == 'ff_dendrite': \n",
+    "            ax.hlines(v_th+2, time_dAP, data[name]['spikes_exc']['times'][0], lw=lw_dAP, color=color_dAP)\n",
+    "    \n",
+    "        # clamp voltage if doesn't reach the firing threshold\n",
+    "        if name == 'ff' or name == 'ff_dendrite': \n",
+    "            max_volt = max(data[name]['inh']['V_m']) \n",
+    "            max_volt_ind = np.where(data[name]['inh']['V_m']==max_volt)[0]\n",
+    "            data[name]['inh']['V_m'][max_volt_ind] = 20\n",
+    "    \n",
+    "        ax.plot(data[name]['inh']['times'], data[name]['inh']['V_m'], lw=lw_i, color=color_inhibit, zorder=1, label='inhibitory neuron') \n",
+    "        ax.set_ylim([ymin, ymax])\n",
+    "        ax.set_xlim([xmin, xmax])\n",
+    "        ax.hlines(v_th, xmin, xmax, lw=lw_vtheta, color=color_hrl, linestyle='--')\n",
+    "    \n",
+    "        axes.append(ax)\n",
+    "    \n",
+    "    axes[1].set_ylabel('membrane potential (mV)')\n",
+    "    \n",
+    "    # set position of arrows\n",
+    "    position_excitation_arrows(axes, p['soma_excitation_time'], p['dendrite_excitation_time'])\n",
+    "    \n",
+    "    axes[0].legend(loc='center right')\n",
+    "    axes[0].set_yticklabels([])\n",
+    "    axes[0].set_xticklabels([])\n",
+    "    axes[1].set_xticklabels([])\n",
+    "    axes[2].set_yticklabels([])\n",
+    "    axes[2].set_xlabel('time (ms)')\n",
+    "    \n",
+    "    ########################################\n",
+    "    # plt spikes of A and B\n",
+    "    # --------------------------------------\n",
+    "    ax = fig.add_subplot(gs[i+1,0])\n",
+    "    plt.axis('off')\n",
+    "    \n",
+    "    ax = plt.subplot(gs[i+2,0])\n",
+    "    ax.text(label_pos[0], label_pos[1], 'D', transform=ax.transAxes, horizontalalignment='center', verticalalignment='center', size=10, weight='bold')\n",
+    "    \n",
+    "    xmin_d=25.6\n",
+    "    xmax_d=29\n",
+    "    \n",
+    "    ymin_d=0\n",
+    "    ymax_d=10\n",
+    "    \n",
+    "    name = 'ff'\n",
+    "    ax.plot(data[name]['spikes_exc']['times'], (3*ymax_d/4)*np.ones(len(data[name]['spikes_exc']['times'])), '|', c=color_somatic_spike, ms=ms_spike, mew=mew_spike)\n",
+    "    ax.plot(data[name]['spikes_inh']['times'], (3*ymax_d/4)*np.ones(len(data[name]['spikes_inh']['times'])), '|', c=color_inh_spike, ms=ms_spike, mew=mew_spike)\n",
+    "    \n",
+    "    name = 'ff_dendrite'\n",
+    "    ax.plot(data[name]['spikes_exc']['times'], (ymax_d/4)*np.ones(len(data[name]['spikes_exc']['times'])), '|', c=color_somatic_spike, ms=ms_spike, mew=mew_spike)\n",
+    "    ax.plot(data[name]['spikes_inh']['times'], (ymax_d/4)*np.ones(len(data[name]['spikes_inh']['times'])), '|', c=color_inh_spike, ms=ms_spike, mew=mew_spike)\n",
+    "    ax.hlines(ymax_d/2, xmin, xmax, lw=0.5, color=color_hrl, linestyles='solid')\n",
+    "    \n",
+    "    ax.set_yticklabels([])\n",
+    "    ax.tick_params(left=False)\n",
+    "    ax.set_ylim([ymin_d, ymax_d])\n",
+    "    ax.set_xlim([xmin_d, xmax_d])\n",
+    "    ax.set_xlabel('time (ms)')\n",
+    "    \n",
+    "    ax.text(xmin_d+0.05, (3*ymax_d/4)-1, 'A', size=8, weight='bold')\n",
+    "    ax.text(xmin_d+0.05, (ymax_d/4)-1, 'C', size=8, weight='bold')\n",
+    "    \n",
+    "    ############################################################\n",
+    "    # add lines between the subplots showing the zoomed in area\n",
+    "    # ----------------------------------------------------------\n",
+    "    xy_C = (xmin_d,ymin)\n",
+    "    xy_D = (xmin_d,ymax_d)\n",
+    "    con = mpl.patches.ConnectionPatch(xyA=xy_C, xyB=xy_D, coordsA='data', coordsB='data', axesA=axes[-1], axesB=ax, color='grey', linestyle='dotted')\n",
+    "    ax.add_artist(con)\n",
+    "    \n",
+    "    xy_C = (xmax_d,ymin)\n",
+    "    xy_D = (xmax_d,ymax_d)\n",
+    "    con = mpl.patches.ConnectionPatch(xyA=xy_C, xyB=xy_D, coordsA='data', coordsB='data', axesA=axes[-1], axesB=ax, color='grey', linestyle='dotted')\n",
+    "    ax.add_artist(con)\n",
+    "    \n",
+    "    plt.savefig(\"/tmp/sequences1.png\")\n",
+    "\n",
+    "plot_active_dendrite_simulation(p, data)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Membrane-potential responses to an external input (blue\n",
+    "arrow, A), a strong dendritic input (brown arrow, B) triggering a dAP, and a\n",
+    "combination of both (C). Black and gray vertical bars mark times of excitatory\n",
+    "and inhibitory spikes, respectively. The horizontal dashed line marks the spike\n",
+    "threshold θE. The horizontal light blue lines depict the dAP plateau. D) Magnified\n",
+    "view of spike times from panels A and C. A dAP preceding the external input\n",
+    "(as in panel C) can speed up somatic, and hence, inhibitory firing, provided the\n",
+    "time interval between the dAP and the external input is in the right range."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Experiment 2: synaptic plasticity dependence on dAP\n",
+    "\n",
+    "We will now test the synaptic plasticity rule and the influence of homeostasis. Synapse growth is boosted if the\n",
+    "dAP activity $z_i(t)$ is below a target dAP activity $z^\\ast$. Conversely, high dAP activity exceeding $z^\\ast$ reduces the synapse growth."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_stdsp_dependence_on_third_factor_parameters():\n",
+    "    DELAY = 0.1\n",
+    "    \n",
+    "    p = para.ParameterSpace({})\n",
+    "    \n",
+    "    p['dt'] = 0.1                                  # simulation time resolution (ms)\n",
+    "    p['print_simulation_progress'] = False         # print the time progress -- True might cause issues with Jupyter\n",
+    "    \n",
+    "    # neuron parameters of the excitatory neurons\n",
+    "    p['soma_model'] = neuron_model_name\n",
+    "    p['soma_params'] = {}\n",
+    "    p['soma_params']['C_m'] = 250.        # membrane capacitance (pF)\n",
+    "    p['soma_params']['E_L'] = 0.          # resting membrane potential (mV)\n",
+    "    # p['soma_params']['I_e'] = 0.        # external DC currents (pA)\n",
+    "    p['soma_params']['V_m'] = 0.          # initial potential (mV)\n",
+    "    p['soma_params']['V_reset'] = 0.      # reset potential (mV)\n",
+    "    p['soma_params']['V_th'] = 20.        # spike threshold (mV)\n",
+    "    p['soma_params']['t_ref'] = 10.       # refractory period\n",
+    "    p['soma_params']['tau_m'] = 10.       # membrane time constant (ms)\n",
+    "    p['soma_params']['tau_syn1'] = 2.     # synaptic time constant: external input (receptor 1)\n",
+    "    p['soma_params']['tau_syn2'] = 5.     # synaptic time constant: dendrtic input (receptor 2)\n",
+    "    p['soma_params']['tau_syn3'] = 1.     # synaptic time constant: inhibitory input (receptor 3)\n",
+    "    # dendritic action potential\n",
+    "    p['soma_params']['I_p'] = 200. # current clamp value for I_dAP during a dendritic action potenti\n",
+    "    p['soma_params']['tau_dAP'] = 60.       # time window over which the dendritic current clamp is active\n",
+    "    p['soma_params']['theta_dAP'] = 59.        # current threshold for a dendritic action potential\n",
+    "    p['fixed_somatic_delay'] = 2          # this is an approximate time of how long it takes the soma to fire\n",
+    "                                          # upon receiving an external stimulus \n",
+    "    \n",
+    "    p['soma_params']['I_dend_incr'] = 2.71 / (p['soma_params']['tau_syn2'])\n",
+    "\n",
+    "    # synaptic parameters\n",
+    "    p['J_EX_psp'] = 1.1 * p['soma_params']['V_th']     # somatic PSP as a response to an external input\n",
+    "    p['convergence'] = 5\n",
+    "    p['pattern_size'] = 20       # sparse set of active neurons per subpopulation\n",
+    "    \n",
+    "    # parameters for ee synapses (stdsp)\n",
+    "    p['syn_dict_ee'] = {}\n",
+    "    p['permanence_min'] = 0.\n",
+    "    p['permanence_max'] = 8.\n",
+    "    p['calibration'] = 0.\n",
+    "    p['syn_dict_ee']['weight'] = 0.01                    # synaptic weight\n",
+    "    p['syn_dict_ee']['synapse_model'] = synapse_model_name  # synapse model\n",
+    "    if \"synapse_nestml\" in synapse_model_name:\n",
+    "        p['syn_dict_ee']['permanence_threshold'] = 10.                    # synapse maturity threshold\n",
+    "    else:\n",
+    "        p['syn_dict_ee']['th_perm'] = 10.                    # synapse maturity threshold\n",
+    "\n",
+    "    if \"synapse_nestml\" in synapse_model_name:\n",
+    "        p['syn_dict_ee']['tau_pre_trace'] = 20.                   # plasticity time constant (potentiation)\n",
+    "    else:\n",
+    "        p['syn_dict_ee']['tau_plus'] = 20.                   # plasticity time constant (potentiation)\n",
+    "    \n",
+    "    p['syn_dict_ee']['delay'] = 2.                       # dendritic delay \n",
+    "    p['syn_dict_ee']['receptor_type'] = 2                # receptor corresponding to the dendritic input\n",
+    "    p['syn_dict_ee']['lambda_plus'] = 0.08                     # potentiation rate\n",
+    "    p['syn_dict_ee']['zt'] = 1.                          # target dAP trace [pA]\n",
+    "    p['syn_dict_ee']['lambda_h'] = 0.014                        # homeostasis rate\n",
+    "    p['syn_dict_ee']['Wmax'] = 1.1 * p['soma_params']['theta_dAP'] / p['convergence']   # Maximum allowed weight\n",
+    "    p['syn_dict_ee']['permanence_max'] = 20.                       # Maximum allowed permanence\n",
+    "    p['syn_dict_ee']['permanence_min'] = 1.                        # Minimum allowed permanence\n",
+    "    p['syn_dict_ee']['lambda_minus'] = 0.0015\n",
+    "\n",
+    "    # parameters of EX synapses (external to soma of E neurons)\n",
+    "    p['conn_dict_ex'] = {}\n",
+    "    p['syn_dict_ex'] = {}\n",
+    "    p['syn_dict_ex']['receptor_type'] = 1                    # receptor corresponding to external input\n",
+    "    p['syn_dict_ex']['delay'] = DELAY                        # dendritic delay\n",
+    "    p['conn_dict_ex']['rule'] = 'all_to_all'                 # connection rule\n",
+    "    \n",
+    "    ## stimulus parameters\n",
+    "    p['DeltaT'] = 40.                               # inter-stimulus interval\n",
+    "\n",
+    "    p['seed'] = 1          # rng seed\n",
+    "    \n",
+    "    return p\n",
+    "\n",
+    "params = create_stdsp_dependence_on_third_factor_parameters() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAABMcAAALACAYAAACXRhXVAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAABJ0AAASdAHeZh94AAEAAElEQVR4nOzdeXgTVdsG8DtJV3bKDoLsKIsgyiIioKIsWgRFEFFBQUVxQ9RPcQEEcUHEDV5XBAUUQQERBRUBZRcVZRUKlKUspbQUStck8/3RpnSaSTJpZjIzOffvulBITmaemWfOOZOTmTM2SZIkEBERERERERERCchudABERERERERERERG4eAYEREREREREREJi4NjREREREREREQkLA6OERERERERERGRsDg4RkREREREREREwuLgGBERERERERERCYuDY0REREREREREJCwOjhERERERERERkbA4OEZERERERERERMLi4BgREREREREREQmLg2NERERERERERCQsDo4REREREREREZGwODhGREREVILNZvP7Z/bs2UaHaDr79u3D66+/juuuuw7169dHTEwMatWqhVtuuQWrV68u0zI3bNiAvn37IiEhAfHx8bjsssvw9ttvw+VyaRw9ERERic4mSZJkdBBEREREZmGz2QAA48ePV3y/f//+aNeuXRgjMr877rgDCxYsQMuWLdG1a1ckJCTgv//+w3fffQeXy4V33nkHjz32mOrlLV26FLfddhvi4uIwePBgJCQkYNmyZfjvv/8wcOBALFy4UMetISIiItFwcIyIiIioBM/gGE+R1Js9ezbatm2Lyy+/XPb62rVrccMNN8BmsyE5ORl16tQJuKyzZ8+iadOmyMzMxPr163HllVcCAHJzc3Hddddh48aN+PLLL3HHHXfosi1EREQkHt5WSURERFRGeXl5eO2119CmTRuUK1cOlSpVwjXXXIOvv/7aq2xycjJsNhuGDx+OvXv3YvDgwahZsybsdjvWrFnjcx2ez5n5Vs/hw4d7DYwBQPfu3dGjRw/k5+djw4YNqpa1aNEinDp1CnfccUfxwBgAxMXFYfLkyQCA//3vf9oETkRERAQgyugAiIiIiKwoPz8fvXr1wtq1a3HJJZdg9OjRyM7OxqJFizB48GBs27YNU6ZM8frc/v370alTJzRv3hxDhw5FTk4OKlWq5HM9VapU8XmL5/vvv4/Tp0+jXLlymm2X1qKjowEAUVHqTjt//fVXAEDv3r293uvWrRvKlSuHDRs2IC8vD7GxsdoFSkRERMLibZVEREREJfibc6xhw4YYPnw4AODVV1/FuHHj0KdPH3z33XfFgz+pqano2LEjDh06hPXr16NLly4ACq8Aa9SoEQDgueeeUxw4C8b48ePx8ssv49Zbb8XChQtht/u/ISA5OTnoK8yGDx+Ohg0bljnGQ4cOoUWLFnA4HDh69CiqVq0a8DMdOnTA1q1bsXXrVlxxxRVe77du3Ro7d+7Erl27cOmll5Y5NiIiIiIPXjlGREREpGDixIler3Xv3r14cGzWrFmw2Wx46623ZFdF1axZEy+++CJGjhyJTz75pHhwzKNWrVo+rwRT6/PPP8fLL7+Mjh07Yu7cuQEHxoDCwTGlbfKnR48eZR4cy8vLw9ChQ5GXl4c33nhD1cAYAGRmZgIAKleurPi+5/UzZ86UKS4iIiKi0jg4RkRERKTA38X1586dQ1JSEurVq4dLLrnE6/3rrrsOAPD33397vde2bduQbgdcvXo1Ro4ciUaNGmHZsmWIj49X9bkePXqE7SEDLpcLd999N9avX4/BgwfjqaeeCst6iYiIiMqCE/ITERERBclzdZOvpy96Xle6uql27dplXu/u3btx6623onz58li+fDlq1qxZ5mXpxeVy4a677sLChQsxaNAgzJ07t/hWVTU8V4Z59nFpnterVKkScqxEREREAK8cIyIiIgqaZwDnxIkTiu8fP35cVq6kYAaKSkpNTUXfvn2RnZ2NlStXBj3fVjjmHCsoKMDQoUOxcOFC3Hnnnfj888/hcDiCWmeLFi2wdetW7N2712vOMafTiYMHDyIqKgqNGzcOarlEREREvnBwjIiIiChIFStWRJMmTXDgwAHs27cPzZo1k72/evVqAED79u01WV9OTg4SExORnJyMOXPmoEePHkEvQ+85x/Lz8zFo0CAsXboU99xzDz777DNVc6GVdt1112HevHlYsWIFhgwZInvvt99+Q3Z2Nrp168YnVRIREZFmeFslERERURncd999kCQJTz/9NFwuV/HraWlpmDRpUnGZULndbtx1113YsmULxo8fj3vuuadMy/HMORbMH7WDcHl5eRgwYACWLl2KESNGqBoYy8zMxJ49e4qvsvMYOHAgqlevjq+++gpbt24tfj03NxcvvPACAOChhx4KbuOJiIiI/OCVY0RERERl8NRTT+HHH3/E0qVL0bZt2+JbHhcuXIjU1FQ888wz6Nq1a8jrWbRoEb799ltUq1YNADBhwgSvMv3790e7du1CXldZjRo1Cj/88AOqV6+OevXq4eWXX/Yq06NHD9lg2+LFi3Hvvfdi2LBhsts9K1WqhI8//hgDBw5Ejx49cMcddyAhIQHfffcd/vvvPwwcOBCDBw8Ow1YRERGRKDg4RkRERFQGMTEx+Pnnn/HWW29h/vz5eO+99xAVFYW2bdvi7bff9rolsKyys7MBAKdPn/Z5W2TDhg0NHRw7ePAggMKr5pQGxjzUXonWv39/rF27Fq+88gq++eYb5ObmomnTpnjrrbfw2GOPlXneNiIiIiIlNilcz/QmIiIiIiIiIiIyGc45RkREREREREREwuLgGBERERERERERCYuDY0REREREREREJCwOjhERERERERERkbA4OEZERERERERERMLi4BgREREREREREQmLg2NERERERERERCQsDo4REREREREREZGwODhGRERERERERETC4uAYEREREREREREJi4NjREREREREREQkLA6OERERERERERGRsDg4RkREREREREREwuLgGBERERERERERCYuDY0REREREREREJCwOjhERERERERERkbA4OEZERERERERERMLi4BgREREREREREQmLg2NERERERERERCQsDo4REREREREREZGwODhGRERERERERETC4uAYEREREREREREJi4NjREREREREREQkLA6OERERERERERGRsDg4RkREREREREREwuLgGBERERERERERCYuDY0REREREREREJCwOjhERERERERERkbA4OEZERERERERERMLi4BgREREREREREQmLg2NERERERERERCQsDo4REREREREREZGwODhGRERERERERETC4uAYEREREREREREJi4NjREREREREREQkLA6OERERERERERGRsDg4RkREREREREREwuLgGBERERERERERCYuDY0REREREREREJCwOjhERERERERERkbA4OEZERERERERERMLi4BgREREREREREQmLg2NERERERERERCQsDo4REREREREREZGwODhGRERERERERETC4uAYEREREREREREJi4NjREREREREREQkLA6OERERERERERGRsDg4RkREREREREREwuLgGBERERERERERCYuDY0REREREREREJCwOjhERERERERERkbA4OEZERERERERERMLi4BgREREREREREQkryugARHbmzBmsXbsW9evXR2xsrNHhEBERERERERFZXl5eHo4cOYLu3bujSpUqActzcMxAa9euRf/+/Y0Og4iIiIiIiIgo4ixZsgS33HJLwHIcHDNQ/fr1ARQmq2nTpgZHE7wClxsL/jiC77cdRuWN7+LVDhlIsJ0rfl+KT4CtzUAUtLsbC/48ju+3HUbvvJ/Qz7FRsRza34MCOIqX6a+s2mXqVe67bSk4l52LwY7VXmVRLgFoLVaM//vHgYfuH6H5tljhmGCM4tR/xui73IcHL8KIl2aYOkYr7Eezt/W+yqVv+wlPXglTxyjCMcH6b+39aNUYfdV/M8Vohf1o1RhL1n8rtPVWiNGIYwLt7wEc0QjWBx98gFGjRgX9uXBJSkpC//79i8ddArFJkiTpHBP5sHPnTrRu3Ro7duxAq1atjA4nKAUuNx74fCt+/+84Pop+CxVO/Ikr6zpgt10oIwGwAdgW3wl3ZDyImdHv4jrHNrglKJZzN7sRD+SNwZq9p/BR9Ft+y6pdph7lBmaMhg3Ahz5jtMEGSagYN6e40KmeQ/NtscIxwRjfFab+M0bf5f5IcSGmaRdTx2iF/Wj2tt5XuU1HXeh8kcPUMYpwTLD+W3s/WjVGX/XfTDFaYT9aNUZP/bdCW2+FGI06JtCsF3DHvKAHyLZs2YKOHTsG9ZlwCna8hYNjBrLy4Ni7q/bhrZ/34lHHtxgbvQiLdxdgwKW+K9M6Vyt0dewMuNw3C26HDRLGRi8KWFbtMrUuxxi9lcy/1beFMQa/btZ/xug5BswcY7DlGKP6cv7aALPEqEU5K2wL67825Rij+nJWr/+MMbRynvybOUYPK8Ro5Lbg2heA7k8HLlfC4sWLMWDAgKA+E04cHLMQqw6OFbjcuOrVVcjMysaG2EdRDZl4fl8r3NgkGtc6tnmVd0sXRuL/k+pju7sx+jvWIcbmkpcDkCZVKhwRl2z4xX0Feju2oJoty+cy86UoLHN3QTv7PjS3H/O77lXu9oiBE90d//ott0u6GHukBrjFvh7RNrdXuTQUxuiU7PjV3R59HZtR1Xbe5zJzpWh87+6CK+z/oan9uN91/+S+EhWQi66OHX7L7ZAaYZ90EfoFiDFfisIadzvc7NiIyrYcn8vMkWLwvfsqdLTvQWP7CZ/lIElYKXVEFWShi2NX8fuz/s7HfZfHyGL8V2qCg1Jt9LOvh8OmvDwbJBxHNaxxtUWiYyMqlYqx5DERJ+Xhe3cXdLHvxMX2VL8x/ih1Qg1bJjrZ9/jdj39LzXBUqoGb7Rv8xpgi1cDv7jZIdKxHRVuezxhjpAIsd1+Fa+zbUd9+yue6JQn4QeqMurY0XGnf5zfGP6UWOC4lING+ETY/MR6RamKDuxUSHRtQwU+M0ZITP7g7o5v9H1xkP+1z3W4J+EG6ChfZUnGFPclvjBP2NkXHptVwk32z3xgPSbWxyX0pbnGsRzlbvs8YoyQXfnB3wrWOv1HXluFz3S7JhuXSVWhsO4a29oN+Y9zkbolMlEdv+x9+Yzwo1cEf7ha4xbEB8X5itEturHB3xPWOv1Dbdsbnup2SHd9LXdDcdgRt7Ml+Y1zvbo1sxKGXY6vfcvulevjL3Qz9HesQa3P6jBGShJ/cHXCjYytq2M76XGaBZMcy99VoaU9GK/thv+v+zX0ZnIhCT8dfsjKz/s7H8HYxxeX2SRfhH3cT3OJY7zdGtwT84r4SNzr+QI2StzaUWne+5MAy99W4zL4fl9iP+o1xjbsdbEDA/miP1AA73Q1xi2O93/7IJdmxyt0+YH+UJ0Xh+yD6o1gUoJtju99yu6SG2CPVN31/NOtgLbzZeDOibJJXOU+MeVIU1gbRH3Wy70Yj+0mf5Xz1R0ox/iM1wSGpNhID9EfHUB1rXZch0bEBlWy58nK4cEzESvlY7r5KVX/0g9QZtWwZ6Gj/z2+Mnv4o0b5B9qt+6XJ69EfLpc6op6I/2iq1wEmpKm62b5K1o6Xrv6c/6ufYgPKq+qNtuMie7nPdbglYLnVBA9sJtLfv9xvjFukSnJYqo2+A/ihZqo0t7kvQz7HBb3/kkFz40d0J1zn+Rp0A/dH3Uhc0saUE7I82ulvhLMoF7I8OSHWx1d0c/R3rEWcr8Bmjpz/q6fgTtWyZPtft6Y9a2A6jtf2Q3xjXu1sjB7G40fGn33L7pXp4N7kR3my0Kez90Vp3W7hhx/WOv/2W2yvVx7/uxqr7o16OLaiu4ruHmv5otfty2OHGtY5//Ja70B+tQ0zpth7e/VEfx2Yk+Gnrg+mPfnFfgTjkq+qP/pPqe3338NR/T1tfIDmw2n256v7oSvt/aBKwP+qAisjG1QoDPCXLbZcaY79UF/3s6zXpj7KlGCxX2R+tkDohAWdxlWO33xj16o+utu9AAz9t/YX+KB0d7Xu9ykmwwVahBjBmV1BXj82aNQv33Xef6vLhFux4iz0MMVGE2ZqcgbSsfFxh34satkxslxrjywbPY7JzqGJ5uw1w2Aov2uyT/zqecT7o9UUEKDwYa9rOoobtLIYUvIjnnSNxSqrid5lvuwbiGeeDWOrq6rfcVqkF7i94Cq867/RbzgUHbs5/FU8VPOT1RcRTzhPjwPyJeN45Emekin6XOdV1B/7P+QB+cHfyW26DuzVGFTyJN5yD/ZbLRzT65b+CpwoeDBjjLfmT8bxzJM4j3u8yX3HdjWedD+An95V+y62RLsfDBU9gunOg7P1uF0fJymUjDv3zJ+Hpgge9Gv6S5ew2oHfea3jeORL58G6ISx4T45334Tnn/Vjtbuc3xpVSRzxS8DjedSr/iuEpdxblcVv+RDxT8EDAGK/Pn4pxzpFww+FdrkSM45z3Y5xzJNa5W/td9zKpCx4reBT/c/bzW+40KuH2/PF4rmCk1wl06Ri757+N55z3wwHv3ztKxviU8yGMc47En1Jzv+v+xt0djxc8gk+cN/ktdwIJ+Pzil/F8wYiAMV6b/xaec96PaPiv/485H8XzzpHY4W7kd91funtiTMFofObs47fcUakm7ix4AS8W3Bcwxuvzp+FZ5wNeA2OlY3yoYAyed47EXrfyHAaeZc5x98bYgocw13WD33IHpLq4u2AcJhQM81vObgNuyJ+K/3M+4HWSXzrGkQXP4HnnSCRLtf0u8xP3zXjaOQoLXNf6LbdHaoDhBc/iZefdXmW6XRwli7FX/ht4xvlgwBiHFYzD886ROCFV87vuGa4BeMb5IL5xdfNb7l+pCe4reEZVf9Q3/zU87Ryluj9Kkyr7XebbrtvxjPNBfOe62m+5P6RLVPVHTjhwc/4UVf3Rbfkv43nnSGRK5f0u8w3XEPyf8wH8GKA/Wl/UH73pHOS3XB5i0C//FSyt94jXF5HSMfbPfwXPO0ciO0B/NMl1D551PoCfA/RHq6X2eLjgCbztvM1vufOIx4Ag+6OCgP3RvXjOeT/WuNv6XfcKqRMeLXgM7zpv9VsuExVwW/5E/F/B/V4DY6VjvC5/GsY5R0JSOIUvGeOzzgcwzjkSG9wt/a77O+lqPF7wKD4I0B+loTIG+eiPStf/C/2RwnFbIsaxzocxzjkSfwfojxa6r8UTBaMD9kfHUQ135L+E51W09dflv4VnnQ8gBv7bqMecj6nqj+a7e+LJgocx29nbb7kjUk0MLXgeLxXcGzDGnvlv4lnnA14DY6VjfLBgLJ53jsS+AP3RbHcfjC14CPNcPf2W2y/VC6o/Wlb34YBt/Yii/uhQgP7oQ3c/PO0cha8D9Ee7pYtxb8H/KfZHpWPsXfTdI1CM9xT3R9X9LvP9ov7oW9c1fsttk5piRMHTeCVAfyTJ+iP/dWZwwUt43jkSpwN8P3rLNQjPOB/E964ufsttkS7BAwVj8bpziN9ynv5orEJ/5Kn/pfujc1I5v8t8zXVnUX+kfEuep9w6dxuMKhgTsD/KRSxuyZ+MpwpGBeyP+uVPwfPOkchBnN9levqjX9xX+C23SroCowsex9ulvh+VLpdV1B+p+e7RK+91PO8cCWeA/uhF54ii/qid33X/IHXGowWP4X0f349skICsVODwJsX3fenWTfm8zKo4OEZBy8wp/NJYGYW/qqxyXQ4AuMru/cttSa4gDrcDUl0AUPxFpqQPXYkAgIeivvNbboWrAwCgm937l5uSsn00kkpSUAMA0EjhSquSZrsKT5Qei1rit9z37s4AoPgLWElpKPyCpnTS6V22CgCgrs37F9mSvnRdBwAYFfW933JLir709Xb8IXt9xhb5QMKxoi+65SD/pUPJORR+mauu8EtiSd+6C09Chkf95LfcQld3AMDNdv+N+8GiE7Tq8P6VtbR8xAAAKtuy/ZZbXpTDIVGr/Zb70lm4vwc41vstt8fdAABQ1+Z9hZcvSoM6JXk6+FscG/2W8wzm3O5Y67fcv+7GAIBGNv/1oKRohcGIkn53XwYAuLHU1UmlfVZUt4ZE/eq33NaiL16XKPwKXVZbpEsBQPGX1pI+dvYFANzj8H/cbnAX/pp1mf2ABtEV+kdqAgDooPALYUmeQdp7HSv8lltbNBDQweZ9BUzpNkCtPVLhMa50VV1J77sKT+buj/rBb7lVrvYAgC52/7cPFCgMdPtyUKoDAGhhT/Fb7iPXzQCAhwP0Rz+6Cr8IdA/QH/n6UUPJMRR+mWuocBVTSXNcvQAAjwboj5a5rwIA9FS4aqQkz4Ch0oC3V9mivqtOgP7IM0j7QNRyv+UWF/VHfRxb/JZLKfqiWxH+228AyELhl7lqClcxytZd1B8Ni/rZb7mvi/qjRLv/9vZA0TFWXeGqn9IKip6lVfpK69I8A6CDo/y34Rf6o9/9ltvtvhgAUM+W5vWer/qvNKhT0ip3YX1NdPjvrz8v6o8GqeyPmti8r5TxJUphMKKkde42AIAbAvRHs1yFP9IE6o/+kFoAAC5VuHKrrLYWLVPpKs+SPi4aXLzH4f+4Xa9Df/RvUX90ZYD+aKbzFgDAfY4f/ZbzDEx3VLhDoKz+K+qPWgfoj2a4+gMA7g/QRv3sKjzfujpgf6T++XiewcVmAfqjT1yFuX44aqnfcj+6CtuJQP3ROSgPdAHe9f84Cr8DKF3FVNIXrhsBAI8EiPE7d+EA3w0B+qNTxf2R9yBoaemoBACorXA1aEmeQdpA5x6egdI+js1+yx2VCr87VlLRH3nOARIC9EdL3YV94T0B+6MeAAL3R8jxv09KmzFjRlDlzY6DYxS0yvGFAwWZqGBwJGQW03urH1QkosjDNoBIXKz/ROJi/Y8w8VWDKj59+nSdAjGG+qFqoiJXNqyK6hVi8GdWc5ySKivcxCVXcs4Iv+VQYm4ClctUW06zGEvcr655jCrWrbacbjH6eD/xy2wsG1IuqP3ouafebzkEf0yozbXa/a1HjGrLBbo20AwxGrkfmevwxuhvPyZ+mY2ld5QzPkaV5Yzsj9SWs0J/pOa41StGteWM7I8CHZBmaKNUt6N+ypil/jPXAdatcTmz9kemiVFlOUO/H6lt61X0/yL3R5Y4Hw2w7uI5xxp0VrHUCxITE7Fs2bKgPmNmHByjoEU77LjnqoZ46+e9+Nx5Q8AKbLcB61wtFSdHl5UD8Lmzl4om5sIyPXw1YJ5ygRoPT7lAt/bYbcDnBWWLMVA5pbknlMoFuo1T1xh9vL9sSDlZuZoKE5QrLS/Qk1PKckyozXV5m/9bPvWM0cPXJzzlAl3aG44YA5VTe9wamWsjY1Rbzkq5Vlqypw0wPEaV5TooTI4erhjVlmsV4LYrM/RHF9v838apZ4xqy3lu2Q/U3upy3Nr8rLhEuUC3w5uiHfVTRu/6r/Y8U2m+NqVyRubaEn2mynJG5jqS+vVAt5qa4RzuUj/TUnjqv9H9UX2b/9s49eyP7CqP20C3SBrZRtkgAR0eCGoyfgARNTAG8LZKKqOHejTBdZfUxP9c/YrnYynNU//+ie+EkQVj8aurnd9y7mY34r+mI/A/l/KksErL9CjdOJQud0SqqWp5a11tApb7wJWID/zGaFOMMdC6U9zKE1KXLrfOpTzZezhiPClVVSw3bEmOrNwm96WqlueZr85XubIcE2lSJVXltrpaGBbjmaJbkkv396XL/e1ualiM5yTl+Y5Kl9teNMeLETFmS7Gqyu0smivHiBiVHjShVO4/HxMphyNGp4/5t0qXS3IXzgWpdNwOW5Kja4xqyx2Uaqkqt1rH/ihQuSNFc44EKvebgf3RMZX9kWduIiNiTPXRH5Uut9l9CYDA7a0ex+1p1f2R8oT04eyPApXb5m7is5ze9d/XxN6ly/1rYH+Uo7I/2mVof6R8TUTpcnvdFxkWo6/5iUuX21/UHxkRo9pyyRHUH/3upz/y1H+92vrj7gRV5TYY2B/5+n5UutyWov4oUDk9jtt0Hw+QK/4G3awX0PUJn8vzZdiwYUF/xsw4OEZlEu2w48O7r8DjN7TErw7lJ3OhfE3g2hfQcswyjL7hMrwU87TfcvY75mPmPZ3w+A1+RupLLVNtufX2DqrKTY55ImC5x29oiSoVlJ8IBqDwktQyxLjV0U5VuTejHzIsxn/tyrl5a8BFsnLvRI9UtbznY571W64sx8Reu/IJfOly/4u+x7AYD9qUB0JKl/ss+g7DYkyxKz9RqnS5eVHKT2ELR4ynbconI6XLLXLcbFiM52w+5mYsVe67KOWnWYYjRs8DJwKVW+no4bPc9EnP6xqj2nKrVfZH42OeMizGjXblpzCWLvdKzOMBy+nV1v/paKuq3JvRowyLcbtd+UeY0uXejR6hqpw+/ZHyYE3pch9GKz9xLxwxJtuUB0JKl5sdpfwk7XDU/xN25R84S5f7MmqAqnJ6xJhuq6Kq3DcO5SduhiPGLPioh6XKLTOwP1J6UqxSuZ8c3Q2LUW25tY6rVJWbEONjECYMMW6yKz+FsXS5KTGP+Sznqf96tfV/OXyULVVuWvSDAcvpFeNOu/IPHKXLvRd1n6pyehy3++yN/JbDHfOCvmoM4JxjRMWiHXY8en0z5LvceO/XJMhubWzeG7bBcwFHNKIBPHp9M4y8phEufWml13JsT+4qroyestN+VrjEeNj3sDXoLFump5xs3UO+hq3pdbJy6dn5+Gx9srxcywGw3faxrNydnRrgism/+IzRU25UjyZo9rzCk3SGfV94r7ZCjG7JBrvn0cJ3L4Wt4dWyckczcrBg6xH5NXBth8DW7z1ZuX7t6qL71DXybbHZNYkx0P7+7+Q5fP/vcXmMV47ArJ018XT3p4vLXd+yJvq+sw6y3+qjy6uOMZRj4s/DGVjz3yl5jFc9AlvPCbJyVzWphoEfbJTvx/I1whLj70lp2HIwXR5jt6dh6/5/snJtLqqM4Z/9IS9XuT5sj/2te4wrd57AjmNn5fvnupdgu/oxWbkmNSvg4Xl/yWOs1gy2hzfqHuM3fx1F8ulseYy9psDW8QFZuTpV4vDUwn/lMdZpC9vIVbrH+MWmQ0g9lyeP8ea3Ybv8Llm5inFRmLBslzzG+p1hG/697jF+8Nt+nM9zyWPs/yFsbW6TlbPbbZi68j95jI2vhW3oQnz61tt4OjFWtxgvtPUl3D4HtktukpXLdbowY/V++ba06AvboM9l5e7r2gitxuvVH5Vw50LYmlwrK5d2Pg9zNhySx9jqVthu/UhW7o6O9dHhlVU+Y9Ssrb/nO9gu7iIrdzg9Gwv/PCqPsd1dsCW+LSt3c9s6uPbNtfJy9qiw9Ed7TpzD8u3H5evucD9svV+Vlbvu0pq46d118rzEVgpLW7/1UAbW7i3dHz0KW8/xsnKdGidg0IebIOszy9cMS4xr953C1uQM+X7s/n+wdXtaVq5VvUq4b/ZWebkqDWB79C/d6//y7cex58Q5+X68fjxsXR6VlWtUozwemf+3PMYal8A2ap3u+3Hhn0dxOL10f/QabB1HysrVqhyHZxb9Ky9X53LYRv6se4xzNiYjLStfvu7Ed2Frd6esXPnYKLz8fan+qMFVsA1bpnuMM9YkIbfALY9xwEewtb5VVs5mA978aa+8XJPrYbtzge4xXihXYt2DvoCtRR9ZufP5Tnyw9oB8P7a4CbZBc2Tl7r26IVpP8H6itebfj4Yugq1xD1m51Kw8fLGxVH/UeiBsAz6QlRvUoT46TVHuj9TUf/X90TLYLr5KVi759Hl881eKfD9efg9sN78lK9f3sjq4ftpa+fLs0WHpj3Ycy8TKnSfl+7HjA7D1miIr1+OSGkh8b718W+Iqh6Wt35Kcjt/3pclj7PIYbNe/VKZBMY9PP/0UTz/t4wIYC+LgGIXMrnQzeMXaXhUt2uHjQkW1FbLRNerKXdzZa5mKMVau51UuSmWMPrdFbYyNrgHs8luZ7EqLrHKxyhht+sRYiuJ+TGiEjp3lv65EK22MPSosx4RijNWaqtuPjpiwxKg4fUKNS7xjVNqP0eXCE6PSfqzVymuZDqVJXmIrGhdj7cvUxRhXxbg2qm47dTGWqxam41Fh3RddqS7GCjUBRzQ6duyoa4yK6nfyjlGxP6pjYH90lcr+6KIyxxhyW9/wGq8OSDHGqgr9kWLHFZ7+SHEemoTG6mK0O8LURimUq95MXX8UFReePlOp/tdoobI/Kh+W+q/cH7VWiNHI/kihXB2F/kipYHyVMLVRCus2WX+kSKE/svvpj0oysj9SbEcr1S3zdw+fVPdHXdQdj0H2R6rqfzBtfamYlPujBt4xKrVRNruB34+aqOyPwvP9SLEdrd48pIExALL8RwLeVklhE3jKQIEEelyJCmomYQyXnJwco0MgIgOxDbAy8/QlZE2s/2RWkgbn21SS9/7UtP4zX5YTae0/B8dIJ4GedROp6xaRDfv37zc6CI3w2KFwiKTjrHBbIqcN0Frk5ToyRNK26CG4/WPu+s9c+xdJ+yeStkUP+uwfY+p/JOXa2tti7vY/eBwcI50YOfJvhV8d1MaodTk9SOjfv7+qcsaxwn5Ui/tRm3UzRm3WXVhOXRugtcjbj4ZQ/Uu9BbaFMWqE9V8bkRSjFVhhP0ZejNrWf/ZH4RX6uo1p//XDwTEKmecWZsnAke9AVdsTWaAYFeeM0Ejg5qdw5ZIUIEZNoikbX7meNGmSqnLhYFP4m79yhsSocv8Yuh+L1x2gHDzljMt14DpTVLdMvB89BQ3djyHUmdJtgF4C7h89G3GVAu9HT679U5wLLkxU138Dd7dNZZ0xQ3+kum4Z8R3J5vUX5WJ+9qPe9V99P2NgnVFbzgr9uspjQg/Bto+B+n89adX26PvdQ93C1Z7rKdG9/lvonFltOTP3R8EK1/lfuHBwjELmOZkLNAeWnid9gaq5Z9VmjtETpc1WMgjvT0k+39Gfr1x/8MEHKsqFJ2JJ4W/+yhkxd1uwdcbYGAOUg6eccbmW1xmlckV1y8T70VPQkBiL/h/wePRTrmQboOccLwGPM5XrlnTcz4H3oyfX/l2I0bzHrbFtlLo6Y2iMUFq3n37dkI7d6y/Kxfzsx9LnAFpT318bdwWG8poVcm2Ffl3lMaGHYNvHgP2/ruf1Aeq1Kb4fqVu4zc+/APX9vx5CO2cO0/moyhxaoT8Klt75DzcOjhEZQfPbWIyVmJioopQ1toW0wFyLRl0boDUeZ9rgfgyvyNvfxtR/K4i8XJMvkZTr4LZF0/qv+UhhJOVFD6Hvn0hr/zk4RiHjbZXqRPJtlcuWLVNVLhx4W6U2eFulNkS5rbJ0G6AX3lYZHrytUhui3Fapd/3nbZXa4G2V2jJz2+MRjtsqda//Jrg9NRCRb6sM1/lfuHBwjIhCNmrUKKNDICIDsQ0gEhfrP5G4WP/FFmn55+AYhY2ec7xoT+9YQ1++7verB/ET9osvvqhjIERkdiXbAEOufgmSFWL0MFNb74sR86eQefAcgMyKLZO2lK450rb+M2NWE2ntPwfHiChkS5YsMToEIjIQ2wAicbH+E4mL9V9skZZ/Do6RPoz8Wd5KlwREAklCkyZNjI5CGzxuKRwiKddF2xIxbYDWIjDXESGStkUPQe4fU9d/5tq/SNo/kbQtetBp/xhS/yMp1RY/bk3d/pcBB8dIM8ZW7TJM0miFgRCFcmHbFj/LLD2ZY3x8fEjL01Kw+8fI41btJM3yD1nzuNVF0Lm2qSqnh0jKtdK2qGoDQhBSu2e2XJutP/LTCgbeFoX3w9wfqS1nzAToIeyfsB+3/ik+IKIoRrPVfyMnQJcxba7N2x9Z6RxORul83U+dCZcyrS3IGDWt/1rX63Adt0Hm2szHbbD0bv/DjYNjpJlATZZJv+cpssL8KUbGWHrdW7ZsMSgSb8Hn2rzMfxQazEL1OtC6rZ7rkm2AHttipR9WA+baQm1U4G2xUGIMYKX9E/Aczs+xYKZzAMAa53BGiqT+KPB3D/NvjZ4hat1/KB074ar/auu1Feq/mb97BMts7X+oODhG4jLymb9qBftsYIOMGDEicCErPGPZCqywHxmjNqywH4uoagO0xlybk4WOW0NE4HFr6vpvpAjMtXDrVssSuVY5aGSF/l8tHrf+aRCjqfNfBhwcI82YeZzeSrexyIsZeUm++kubx4wZo2JxvCQ/WFa61a5M5ULF2yr1E+S6VbUBIeBtlToK4TYWK91WaQRRbqvUu/4r4m2VZRZJ/ZFparxpc12WDxnZ/1vgtkpNbvM3CQ32jyHtv444OEYhUzxhMqnAJ396rlvdFybTnNQp8BXZnDlzFEuaej4vI39MMnGOPTwRqv1ybORxG7BeWyjXhuxHDX7w9m4D9KH2S52Z21GPgDHquAluybzto1oXcq2unJECt1HGtaPBrlFpW0xT/w29SERdDi+c64WfFrkOFzPn2kPt/jFzf6T2u4eR/b8Vzpk9Ap+jGLcterU94Wr/w4WDY6QTc16VFXhODXXlykLtPDRmvldeOTIJiYmJiiXNPVeO4qu6xOK9FqX1mCvvF+qCujmHjJ3Pyz9Df/AOMteG7MeQfgQtfLFkG6DnHC9q58rRet6vsgsh1zr2CXabtu2jEcet2v7aV78VTmrnRTJyP6qltC3e5wD6MHW/rjKHF871wi/YuhAJ53B6Zl/t/gncb4Wp3ivsNLXfPfyV07v+W+I8SuW6jTz/16vtCVf7Hy4cHCMygslu/VAURIzLli3TMRAiMju2AVZmgf7IEjGKi/WfSFya1v8I+34kgkhr/zk4Rjox9vcmX6xwibipL7/28ar3/ebGb0PZLhEPT9zKl1Ubv89K4m2V2gg219a7rbLwxXDNOcHbKrVRttsqzbVPQ7utMrzbEum3VZqm/hvZr1v2tkprnzOXelWXWHzhbZWFdJ9z1ALnzB5lu60yXG1U4f+1rtecc4xIFXPeVlm2cnrQOkY9tkX9ukePHq3h8vQg4jGhh0iK0UiRtB8Ly6lrA7Rmgf1ohV+YVcdo4P62QoyqWSFGtVj/tVk3Y9Rm3YxRm3UHF6O29d8Kbb0VYlQr9HUb0/7rh4NjFDK1V2Xo+R1Bq0VbYd4vz68OZorxt99+U3zdTDGaEfcOWZHScVuyDbDCcR22OV5CYYH+yMMKMZo/QuvydQ6gFTNMvk7hoXWqjfx9wgzfj7SmtEl61/9gGdJcCNxGmS3/oeLgGIXMdI266QIKhTVa26pVq6ooZY1tIS0w16JR1wZozQrHWST1R6QNKxy3wTGm/ltBJOU6krZFDxbYP6q/HwW3Leau/xbIi6FC3z/mzn/wODhGmjH3PfUmi03zW0R04CfG0u/Uq1dPzQJDCke94HJt5uPWzKH5F55cW2n3WOGx9KFQ1waUnfLuMefAU9nmwDKSv4jVzfGmv9BzbcR+t9ZxW/Y2Su/6Hyzz9OtmzXUAisk267Yw1/6Ubf8Ety2a1v8gvnuUptxGGXmbv59FRNBxa7b2P1QcHKOQWetLnZGThqudSNq8fA0yrly5slS5cETjX+AvqMYFGez+MWaSds86zX/cmuckw1vwuQ4/1Q9f8PN+6TZAL2pPjs18THiY+SEWZhzCK02LiaTDx7wPktCiPzJN/Q9LFP7XLQV62AUMfPiCFc49VK7bDNXaCv1MIBeOx7LTu/6bow1Xx8z9kV79TLja/3Dh4BjpI6JubSS/JAljx441OgptGHncss6II5JSXXTcRkwboLVIqtcRtS1GB2ByQeba1PU/ko5bPUTS/omkbdGFPvvHkPofSbm2+LaYuv0vAw6OUdjoOQGyZqPgYZsAuezL9/zqYKYYhw8fHvIyRBQJvzqSeJSO25JtgBXO8ywRY1H7qXsrocHOsEKMpB/f5wDaMPJKbwovradBMfLhK2q3RFL4m9n4e2CZtvVfi/4o/N+Pgj1qrfAQG7X0bv/DjYNjpI8wXzcaSY1MIKb7jmCz4euvvzY6Cm0Yedxa6bpxCo3FU6103EZMG0C+WbyNkh+3xsVhCUHmWu/6H9IAh8WPW92ZbP9IoZzkmm1bjA4gTAzp/yMp1ybblmBF2vkfB8dIH7w9TRyShMTERMPWberlWWXdFF6RlOqi49awNoDCJ5LaqAjaFF0EmWtT1/9IOm71EEn7J5K2RQ867R9D6n8k5dri22Lq9r8MODhGmrHcxJ0WHQgJ2w8Mfp8YIw9i2bJlekejmvJDltRvSziVaQJ0ix63elC8/UJtrsO8LWV6QIRJc6103OrdBoR0q03Y92MZJuQ1tG5p/HQwXbZF5TJN10aF8OGwt1FlP4/Svf4H2T6a6yhQwQq5DpOQ+nWTMcN3j3DsH03rfwi51vqWXN+BqLyt0mT9kV7M9B1QCxwcI80EurXR0O95qsuFaY4XDRj7VCb5Hh03bpyPcuEX7HFm5ltyjZwrwwqCvf3CyFwHqgtWz3XJNkCPbQnpVhul5Wm6tOCWrnZT/M3xEi4Bj1sTxGhmVvpuFPAczs97vs4BjGKFczgjhZJrWTkTHOBm/u6hlmc/6tGOarVMz1KUlheu+h841/rtR61FUhtltvY/VBwcowikslG0+D3eZjJkyBBjVqz2rCeScm3oc6AtsB/VxhhJ22KCdRvWBqhh5H5kG2Wadev+hUnrb+FWOCaKYjSk/lth/6gVSduih0jaP5G0LUVM3f8bKQJzrSTS8s/BMdIML20OhhVuEVF/afP27dtVLC4822Kl2yoDsdKtdmUqFyLeVqmjINetqg0IQbC5LlM5zVjstsoQbk8L27aYNtf+hbR/THb7lb/zKL3rvyIr3FYZgbk2hBXO4Syaa+UPGdn/a3xbJW/z90+DGA1p/3UUZXQAap0/fx7ffPMNDhw4gIyMDK9LeW02G9555x2DohObclPFTtR73f552nTTdPQK1OY6fFsQSica3PK0FOy6jTgmPGu0wnEbOEbjYgu2fTQk10X7x8x15sLa1M3TZ55TUnN+gS/cj74jMEOuA7nQRgWqM4ojVBpHg8JzDx9tjdq6ZcQeVhyYN1muPco251CYtkVlDo3sM4PNtanPRw0+h5Mk9fvHNPtRaa4sE5zDuSUb7DZr90cegX8ILcuntHHhXI/8scTg2KpVq3D77bfjzJkzPstwcMz89KyMWjXqYZs/JZQJ+Yv/b54Y27Rpo/i6Fe77N5JpTpjIMswwx4vS6V3JNsAUIQZwYT+aN1h/c7zos6ays0KMpB9f5wBaEeTuJMvRo62PqFSrPHAvtPXm5e/cQ9P6r8FBpft+VBxkDG6tYfkOF6aGU+/2P9wscVvl6NGjUb58eaxcuRJnzpyB2+32+uNyuYwOk2TM3MSTtmz48ssvjQ5CI8Y+5oBEEUm5LtyWyGkDyLfIO27Jl+D2j7nrP3PtXyTtn0jaFuswpv5HUq6tvS3mbv+DZ4krxw4fPozXX38dN9xwg9GhkGrmnCsrEphv6yRMmTLFoFVrvTd43FI4RFKuC7fFsDbA7KxwGZ1q1t4W+dcPA+ehsYTgtsXc9T+S8qKHSNo/kbQtetBn/xhT/yMp19beFnO3/8GzxJVjl112GTIzM3VbflZWFsaPH4/evXsjISEBNpsNs2fPViy7e/du9O7dGxUqVEBCQgLuvvtunDp1SrfYrMTUVdt096ubem8V8RejfIcmJibqG0oQrP37i5z55lkw13Eb9Hxekk1VOX2Yfz6vUNatdxsQWr022cMXjJwXSYm/eTqlAMdtOOfzUldQ+3WHJJQj11zHrb/zKGPOAaw5V5YZlG1+SSOZc+7GsjFZvdaAtvVf/XeP4N7VkgX6ozD+MGem74BasMTg2Ouvv46ZM2di69atuiw/LS0NL7/8Mnbv3o22bdv6LHf06FF069YNSUlJmDJlCp566iksX74cN9xwA/Lz83WJzQo8nWigRsnQOV7UPlik6P9WmCvL2Bjl6162bJliKSNiDHaNZs51RF10ooOgc+1nwlf9+V+3SR90pZqvNkArel0jqkf9V98XalNOT4HqjFS0/0z3Xdo0jM+hWgFz6GdT9K7/wTJzv24GgfaP6v5Ig1hCFbi9DUsYIdFzvmOt2mZ/fWb46n+g/qiQFep/JPWZZmv/Q2WJ2yq7d++Ot99+G1dddRUuvfRS1K9fHw6HQ1bGZrNh6dKlZVp+nTp1cPz4cdSuXRtbt25Fhw4dFMtNmTIF58+fx59//okGDRoAADp27IgbbrgBs2fPxgMPPFCm9Vud6Toe0wUU+QYNGoSvv/7agDUz10RmYFwbYHZso4Qh8LkH6z+RBejURrH+iy3S8m+JwbFvvvkGd911F1wuF44ePYpz5855lQn2KRElxcbGonbt2qriuPnmm4sHxgCgZ8+eaN68Ob7++mthB8c8TH1aaLYh+hBuEQnb+bffFcl3qK/bkEstMJRoVDNbqkNhvlsb1DJnrgPdIqYvq93GEhx1bUDZGfn482CV7bZKI5X9tsrwscITNZXX6s2ax62/Jkzv+h8s89xWadZcW+02f9/ME5V5IikpHFFpWv+D+O6h7l0jb/M3Uvhu/TRb+x8qSwyOPfvss2jRogW++eYbNG/e3JAYUlJSkJqaiiuvvNLrvY4dO+KHH37w+/nU1FSvucmSkpICrtftduPkyZPIy8uD2+0OLugwaV4uG2M7V8ZFtqtxwF6n8MUqFwMHDsjKFbjcGNu5MgDggOOlC2+UKgcg6HIn7c8gw+YsfPHYaSD6vKzcZZXyMLZzZTS09cABe6PCFys18VpmvtONRzpUxvm8ApzNuBIVT23V8JJkdXO3mOekToGP0KZNm4YXX3zxQjETfPkz835UnqfHN0O+0tk861Z7Em3c/g5ct4wTbF0w4ri9kOuyK90G6EXt8Wjm+n8B60woPHUr8GCk/rEEEjBGA/d4sPtH6dgJV/03z5xDvtdt5rYn+FwbR+05sxFsKNw36ocfTHxMFP0/lBhDrf+B96N5919pZp7Pz6bwNy2Er/0PD0vMOXbs2DE89NBDhg2MAcDx48cBFN6CWVqdOnWQnp6OvLw8n5+fOXMmWrduLfvTv39/AMC6deuwdu1aTJ06Fenp6Rg2bBgA4NZbb8Xhw4eRnJyM3Nxc5OXlIT8/H06nEzk5OZAkCVlZWQBQfDVddnY2nE4n8vLykJeXB6fTiezsbFmZrKwsSJKEnJwcOJ1O5OfnIy8vDy6Xy6vs+fPn4Xa7kZubi4KCAhQUFCA3NxdutxvnzxcOQNWqEIWrm1ZHs6bNgMY9gMY94K7W3KtslN2Gq5tWx9VNqxeXQ+MeitukVE5pmzzloppcU1zOCYfXNjWsXh5XN62Oxk0vuVCuSiOvbSrIy0H3Zgno1SIBKe2exOHLxsIZVR4zZszAihUrZHlSkpSUhFmzZmHx4sXYsmULJk2aVPxeyV+tJ02ejO3bt2P+/PmYP38+tm/fjk2bNnmVK3A6MWnSJGzZsgWLFy/GrFmzkJx8sKicXHp6OqZOnYq1a9dixYoVmDFjhmKMo0aNQkpKimybfBkzZoxsm9LS0rzKzJk9G7169cK4ceOKt8lz/7k8RhsGDRqE7Oxs2Tb5MmzYMNk2KRkwoL/XNp1MPVm0Nnl365kw0rNNntuwS5Y7cyZDVtazTUo8ZUpuky+esp5tSj6U7FXms88+k5UdNWpU8f4uuR9dLndxG1Fym5RkZ2dj0KBBsrJKxo0b57VNLpe7aN0X9s/x48eL1+Up+9Zb04vevVDuVNpppKSkYNSoUQHXXbrd81XWU59KbtPhw4e9Yvz119XYvn27bJuUTrzS0tIwa9YsJCUleW1TaZ76VHKbfJUr3ZYrzRu1bNl3sjYiOzsbH374ode2nD13TtZGjBs3zudJZMk2ouQ2lVa6jUhJScH5rPNF677g5MmTXu2ep90t6a+//kJSUhLy8vKKt+nVV19VXHfJNsKzTUpKtxGzZs0q3o8l98+YJ8d6tXubN2/2Kgd4t3vr1q332ubi5ZZq95SUbiPmz5+vWA6S5NXu7du3z/NmcTFPe1Sy3duwYYPitpRsIzzb5Evpds+j5DLvvfc+r206fVqhj5Mkr3bv7bffUYyx5PI826SkdBvhq36lZ2R4tRGeeWhL5vCXX37xaveUfuR3uZyK7Z4SNW0EAPzvfzN9tnslY9y7L8mr3XuiaPkly+Xk5Ci2e0pKthH+4lRqI/7551+vcrt37/LZ7pXMdcaZM1ixYgVq1KjhtU2llT43UmpPAHi1EZ5tKnRh3Qu+/tqr3fvk00+8lud0OhXbPSWlz42mTp2qWK50G7FlyxakpZ322j+AzavdO3z4iNfyduzcodjuKSl9brRixQrFcoB3u5eb6/095cknn/Tapm1/byuKviSbd7v35Zc+11263VNSuo2YOnUqnC5n0dr8t3vr1q/3Wt7GjRtlZceMGYOk/ft9xleyrPw4U47Ts03u4v7ogokTJ3ptU05Ojte2nD9/3quN8OSg9DarOTcC4NVG+Cq7e/curzZi2fffe617+44dXu2er3nRZsyYgcsvvzzg+Z7S91yPkvvx++XfK7R73o349n//9Woj7r3vvqJ3L5SXoNzu+YpRzfnehx996HW+p/QdznOeKWv3cnK9yuXk5ARo9y4o3Ub4utBG6TthTm6u1/6ZO2++YrundOwptXuTJk1C9+7dFeuTr21S+v7k6xxWqY3wlSelc6MVK1bgjz/+UNxHvtgkM8z4GkCPHj3QuXNnvPbaa7qvyzPn2GeffYbhw4cXv/7777+jW7duWLBggdcJyksvvYRJkyYhIyMDVapUUVyuryvH+vfvjx07dqBVq1Zenzl+/DjOnDmDhIQE1KxZ0xRX4yhJPZuLE2dzkWA7h4tsRYMn8QlA1Ytl5VxuN3YeOwsAuMx+8MIbdS/3Wua/R88EVa6l7RCibEVX1tVqDTiiZeWOZ+bg1Lk8VLedRV1b4QkMytcAKl8kK+d0ubHr+FnY3E5UyzmMzIx0VN81CzXu8/7i0/DZ5QCA5Lg7L7w4wfupqp5ye2PvRozNVfjiMweBcgmyci8s2Y65mw7jEcdiPBW9sPDFa8YC178kK3ciMxedX12FisjG9riRqtatNkZZueeOArEVZeWeXLAN3/6dgmejvsSoqKIJGK8fj8VpjTBgwIDicgdOZeG6aWuRgLP4K66oc4kuBzx/XNsYXzgFRMXIyj3w+Vb8tOskJkXNwt1RvxS+2PdNoOP9snI7UjJx83vrUBunsSnu0cIXy9cEnt6H0kKKUaHc0E82YX3SaUyLnonbHOsKX+z/AdBuiKzcpgOnccdHm9DIdhyrY8cWvpjQGHjsb91j7D9jPbYdOYOPoqfhRsefhS8OngtcKj/xWbX7JEbM2YpWtoNYHvt84Yu12wCj1uke4w1vrcW+1CzMjX4FXR07C1+8ezHQ5DpZue/+OYbHvvwbnWy7sSC2aBDz4quBe72v+NU6xi6vrsKxzFwsiXkR7exFJ+kjfgHqy+e2/HLLYTz37XZcZ/8Ls2LeLHyx2Y3A0IWyci63hCbjftA0xrYTf0JmTgF+jnkazexFXxof3gTUvFRW7pPfD2Dy8t1ItG/AezHvF77Y6lbg9s+wePHi4jYgJ9+FS19aoWmMTcf9AKdbwvrYR1HP04Y/sR2o0kBW7p1f9mH6L3sxxLEKr0Z/Wvhi+3uAfu/JymXmFKDtxJ9ghxsH4u7SJEZPuX9iR6KyreiL/9i9QMVasnJTftiNj347gBGO5Xgxel7hi50fBnrLBxXTsvJw5eRfEIc87Im7V9MYk2LvutBnKvRH4xZvx/zNh/G44xuMif6m8MVuzwDXPS8rd+xMDrq89isq4Tz+jSvRxmreH6UAsRVk5Z746m8s2XYM46Lm4YGows/ghpeBqx+XlUtKzULPt9aiGjLxZ9xDhS/GVATGHdU2xhfTvM49Rs7Zil92n8QrUZ9iaNSqwhdvmgZ0GCkr9+/RM+j3/nrUwWls9PRHFesAY/doG6NCuSEfbcLGA6fxdvT76O8oHJDFgI+AtoNl5TbsT8OdH29GE1sKVsU+XfhitabAo3/K6r8eMd7y/jr8czQTH0e/iRscfxW+eMd84JKbZOV+3nUS93++FZfZ9uO72KIrGeq0Ax70/oFN6xivm7YGB06dx/zoyeji2FX44j1LC3+QLWHpthQ8/tU2XGXfiS9jXilayTXA8O91j7HzlFU4cTYXS2NeQFt70Z0TI38FLrpCVm7e5kN4fvEO9LT/iU9iphW+2Lw3cOcCWTmny42mz/+oaYxtJqzEuVwnVsWMRRN70Tnj6C1AjRaych/9th9TftiDW+zr8E7MzMIXWw8EBn4qK5ed70TLl1ZqGmOj55ZDkoCNsY+gjq1o4P+JHUCV+rJy03/ei3dW7cNQxy94JbposPOK4UDiO7JymdkFaPvyT4iCE0lx92gSo6fcv7EjUMlWOEiHp/YBFWrKyk36fhc+XXcQ9zu+x/PRRd91rnoE6PWKrNypc3no8MoviEcudsfdd+GNCZkh1/8DsUNh9zz85f+SgfiqsnLPffsvvtxyBGOiFuHxqG8LX+z+LHDtc7JyKWdycPVrv6ISsvBvXNFURzY7MD7D57rLdEyMOwbElJeVe+zLv/HdP8fwQtQXGBlVWCdw42Sgy6OycvtOnsMN039DDZzBH3EPF74YWxl47rC2Mb54GnDIbxC8b/Yf+HVPKqZEfYw7o1YXvnjzdODK+xCK0vk3m507d6J169Y+x1tKs8Rtle+99x4SExPRvn17v7+c6Sk+Ph4AFK8Oyy0aifWUUVKzZk3UrFnT5/tK8vLy4HA4TD0wZk4azE1id6BOBSArrxKyEtqghiZRaXNbpaTB9pWZj9AyMuQdT/iOVz9z5Zj4MmzeVqktM98iJsptlaXbAL1Y77bKss+foicz1xm1gr2tUvdt8vNbc6TfVhmu+s/bKkPD2yq1YdnbKhXaKC2O21DrP2+rDA+9bqsMX/sfHpYYHBs6dCicTieGDBmC+++/HxdddJHi0yr/+ecf3WLw3E7pub2ypOPHjyMhIQGxsbGartPtdsPhcFhmYMz0lyAGyWazweFwQHLEBC4smNK57tatmyFxKLFIdVElgjZFF5F1om9tercBVqrXkZRr03yps6jIOm59v2+mcwCAx20gZh6sDRZzHYj++ydc9T/SvmdGCrO1/6GyxJxjCQkJaNasGbp164b27dujZs2aqFatmuxPQkJC4AWFoF69eqhRo0bxHBclbdmyBe3atdNlvVYZGAMCN7+eRs3MW1S64dUtVg3uZtb9yVt+Yiy9X3zNeWPE08EUww5iWzQXQq4VP2nknfAmuws/pFyHeVsC1QW1uQ7bTAhBrqdkG6DH1a3B5jpgOR/zp2hBu1yrW56e1G+L3jGGkGsDBRuObH+bro1SqkeFr/mb9043ftt6cx0HAYU91/6pbcPDFnUIuQ7fri17G+XZ3/qcj2qzA/zFqGn9D+F8/cLcpDoL5dzjwpuahGLc8i8wpP3XkSWuHFuzZo3RIQAAbrvtNsyZMwdHjhxB/fqF95WvWrUKe/fu9TvBH1Gkmz59ujErNtkXISJRGdYGmB3bKIGIm2vWfyIr0KeNYv0XW6Tl3xJXjgUrLS0NjRs3Ln5qiRrvv/8+Jk+eXPx0mGXLlmHy5MmYPHkyMjMLJ78bN24cypUrh2uvvRbvvfceXn31Vdx+++1o06YN7r33Xn+LF4K4p4VlYYVfwdXPn+LvKToXPhSebTHdxZZ+tztybr+SCdNhy9sqjaG0LaragBAo59qcvY7lcu13riyTRGvRQUbF49akV/+Gclul3vU/2Maex61/ZbqtMlzncEGWN3OuzXE+WoYggsy17vW/SJmOQF2OW7PmugRNrm5TJ1z5DxdLXDkWLJfLheTk5OJH6Krx5ptv4tChQ8X//vbbb/Htt4VPxLjrrrtQuXJl1K9fH2vXrsWTTz6JZ599FjExMbjpppswbdo0zecbo0ikdiJp/4w81/J1crxs2bJS5YyndpJmI1xYt9pbFwyYpF3lui88SMI4Zp4ANViGPHyheN2B9qOtqLx3lKXbAKNYYVJsDyNjNHP7qJb6Nsrzf+NaKTO3UVpMyK97/S868THzcav+ARHqHr6kBysMPEnF/zdznbEBkhT4AVpFG2POIdJCWjyQJ9T6LxU/4kCZFfqjC7kOFKzxD1/Rul6b5fxPKxF55VhZJCcnQ5IkxT8NGzYsLteqVSusXLkS58+fR0ZGBubOnYtatWr5XjCFX6n29ZVXXsGwOwbi2subo95FF2PCtA+MiassrNAjQMKwYcMCF9NlW7Q+5TDnlXqWY4XD1lARlOuibVHVBggp8nIdGXTYFrW/XFmkXw+Gueu/kb8oRlCuI2lbRKVTG2VM/edxqwkN9o+52//gcXCMwseguv/CCy/gn7//xCWtLjMmACVWmMwxiEvUfN1vbuQE6GrpfzVB2W+rtCxzpjoimGHXKv3qWLINCFtVD+lhF56Je82wR5XpO0mzfE1l/mS4JkA2cZ6CZuRE8jpVTt3nnLHEF1wFJj3vKRM/Dw3RktaZDl8Gyn6rnRkeviIT5ENsNK3/ofTrSjHqcZBqcAutafpMDfYP5xwjspiDBw9i238HMeXdD40OJULZ8OmnnxodhEaMPAG36Mk/lUEk5bpwWyKnDSDfIu+4JV+C2z/mrv/MtX+RtH8iaVusw5j6H0m5tva2mLv9Dx4Hx8gQi77/BbZ67bF2459e73344YdoW78q9u3ZheFPjEeFZlfjcMpx3HzzzahQoQLq1atX/NjYfbt3YuTgfqjcrDMu7tgX8xf/6LW8krfFRgKT/K5UgoSOHTsatGreVklWFEm5LtwWw9oAs4ukK0csf9xKPv6ux/KtLrhtMXf9j6S86CGS9k8kbYse9Nk/xtT/SMq1tbfF3O1/8Dg4Roa46fquqFC+HL5e9pPXewsWLECT5peg2SUtAQAutxt97noU9evXxxtvvIGGDRvikUcewezZs/HQ3bej1WXtMGXcE6hYoTzuefwlHDx4UGUUHAjxT/1TrYJ5+IXelB8aYM6ngwViU37UWdljCZm5jttgcy0/bsO7LWWbSNo6uda7DQitXoc71wF+BTZbrv0+wdAkVLejpokYQKjXA4R3AK9sE6AXLs+YcwALHLeqhTti8052r8wC01OYtI0Kx9q0rf9+6rVkklyb9NxDvurwrdtM3wG1YNrBsYyMDKNDIB3Fx8ch8YZuWLR8FVwuV/HrJ06cwNq1a9Er8dbi13Jz83DXrX3xv//9Dw8//DCWL1+O+Ph43HfffXjqpckY8/zLGH3vHfjmo6lwuVyY88VcIzYp7MzSRQDA/v37Zf/2NMnGzueljmnmePDDCjFSeJj1QqSSbYBJQ5S5MDeJ7msK+aNmrv+mmyvHSBpUTvMNSqhT+hyAQieZtbEvQbJAvb+wH80ba9jOmUOap7OQUoxmq/9W6I+sEKNaZst/qKKMDsCX2rVro2/fvhg6dCgSExMRGxur+rMxMTHo3r07qlatqmOExpq4bCd2HTtrdBgAgEbVy+POTg2C/tzgfjfiyyUrsGbNGlx//fUAgEWLFsHtdqNXvwGysiPvvPDvKlWqoEWLFkhKSkKvxMLXJdjQomlDVKlcEQcOqL1yzKJnoSbUv39/o0MIwJy5LtvjlDkvmn9qY4ykbQnvupWOW3O3Acy1OCJpP1phWwpj1Lv+K+8JK+wftSJpW0KjfMW8NfeP8hXP1twWf8zd/4dH5LdRvkVa/k07ODZw4EB89913+O6771CxYkXceuutGDp0KK677jofDecFVatWxerVq8MUqTF2HTuLzQfTjQ4DAFDgcpfpc717dEHlShWwYMGC4sGxBQsWoF27dmjYuGlxubi4WNSoJh/orFy5Mi666KLiY8EzAl+5YgVknFF71aE1L3cN26+JQaxn0qRJ+OCDD3QMJlTmvD2tbL8cmXNbzMMCl7urZs5cKx23hrQBJr2NpWzMmWvzCD3X1vuaEp6nrIW4QAD613/lqK1w3KoVSdsSGuVzXCvsH4WneSrGbYVtCY6m9d8CV0wqPrlVuaDekfgRvnWb/ztgcEw7ODZv3jzk5ORgyZIlmD9/PubNm4c5c+agVq1aGDJkCO68805cccUVRodpmJZ1KxkdQrFG1cuX6XOxsTHo3+taLF68GDNnzsTJkyexfv16TJkyRVbOYVe++9fhcCi+btZL0QPOOaTwN7NRngJLMq5R9DtXToA5NZTeN/LYMdlx6xl4DhhV0W4s21Vw4aH8622Y1u2jzvj5hF6h+F5jca4D1RkFRdsSrjZAkmx+d5Gt+Hg0CZPO52W59lGJzesv6umyLWXvj4ykRa7DVv/LMldWmI5bz6rVtqNGzJ+keHFBCOdReipT1kyWaw/z1H/v/XPhmCh7jKHW/1DOPcymTPPLhm1bVJ7XBymSBsYAEw+OAUB8fDyGDBmCIUOGICMjA19//TXmz5+Pt99+G2+//TaaNWuGu+66C3feeScaN25sdLhhNT6xldEhFEs9m4sTZ3PL9NnB/W7EnIXLsGrVKuzevRuSJGHw4ME4F+RytO94tG+oAkUoKfzN//LC3zH4ar8TExOxbNmyEuWMn+Mh0P7x/KJn5Bwvao9bI0L05DDgui0w55DaeVH0GFgPfpHqjlstXch1oHV7/u99VJRsA/Ss/zZbgBhVziUWvnkRfTMyRrXto1bL04Xk9Rf/xQykfv8Y0K9rkOvS5wBau/D1XV39N4La+lpcTmVbpqVg+zgj9veFO0K0oUc7qnaZnoHnwPvRuANXbX/tr28Ntf6rrTOBhG8uUW9qhxiNnadT5Xl9kPRu/8PNtBPyl1a1alU8+OCDWLt2LQ4fPozXXnsN5cqVw0svvYRmzZqhS5cuRodIZdDzmo5ISEjAggULsGDBAnTs2BGNGjUyOqwwMO+JclmoaxStsS2aM+mvW/oScZvFFkknRsKxQhtlhRgtQZ/9yPrvSyQdt5G0LTqIqDYquG3Rtv7rc+u3+ZepsTBOOxFp7b9lBsdKqlevHp5++mnMmTMHt9xyCyRJwubNm40Oi8ogOjoat956KxYuXIjNmzdj8ODBmq/jiy++wNtTX8MXH80AAPy26S9MfvsTTJ76Lg4dOqT5+nyJ2NsqAYwaNSq8gRTT+LZKKsbbKjVad9CrtthtlUXC1QYEuhXJdLdV+m2jjBMR7WMot1XqwaS3pwWiRa7DVv/LcltlmFj2tko/LHdbZZhY9rZKhTZKi9sqQ63/oZx7mI2Zj1u9bqs07jugPkx9W6WSw4cPY/78+fjyyy+xY8cOSJKELl26YOjQoUaHRmVsvgYPHoxPPvkENpsNgwYN0jgm4NNPP8XatWuL/716w1as3rAVANC15024+OKLNV9nQCFNyK9hHGVcUel3XnzxRX1jCYKx9/MrKfu6w7YtkqTuW4XJfhlV3j++y8veCvO2WG6OpyDXrXsbEEpdCPNuDPxDiMlybdG5shT52Y9G3MYS0mCNXm29r7cCfNTfvD+GnANYdDBSken6I5Mx6dyNZWKyXGtB0/pvhXqt+tzDnP261sz0HVALlrhyLC0tDTNnzkTXrl3RqFEjjBs3DgUFBXj55Zdx4MABrFu3Dg899JDRYYor6LZKXmF79uwJSZLgdrtx0UUXyd6b/fZEZO1b77WENWvWYMeOHV6vJ29eju+XLvYqe/xMNv45koGjRw9BSvmr8E/GYfTo0UMxQpM0v36ZZ44nCUuWLCn1SiHd96MmHY/k4+/hZq4vdSIK33mMBQbHVSsMqGQbYIb5vAJRO8+aBisK+aPm6Y+8t8V8MXpTToE5b7UxT50Jrj8qfQ5gLmbZp8EJX9TW3D/KLNpGFf1f//ofQn8E332mMfVfIdd+YjQPpXm/zBxvYOZu/4Nn2ivHzp8/j8WLF2P+/PlYtWoVCgoKUKdOHTzxxBMYOnQo2rdvb3SI5GHtOk0aaNKkidEhWJL5BjqIAlM6bNkGEIlL7/pv5O2SFF6RlOtI2hZ/2P8Hf8tyJIm0/Jt2cKxmzZrIzc1FhQoVcOedd2Lo0KG47rrrYLdb4mI3ogCsMCqiNkYb4uPjdY0kfHTo3FSPgBn52EyVt1WSRiJpXxduizFtgBXaUQuwShulqUiqg8HQJ9ea1n/N+yNRc61WJO2fSGqjrEPb/t8C/ZHmjPzuEbrI+Q5YyLQjTT179sSXX36JkydP4rPPPkPPnj05MGZWkdQ+6SjwRLLqJsU2knJkErZs2RLmSC6s2/c7ZZm4NzydifIvTOY6sQp6cmFDJ+418STNQR5nRuzHCzkMUM7PtoSrDQgco9naUXPl+gLzto9qeeZuU/9VSudb6EOZz8tCbZSS8NX/sjxIIlz9umdtfGiIFtSeM5f+VDgEW19N0x8pTcjveSuEGEOt/6Gce5hN2fajudqoYBn3HVAfpr1ybOnSpUaHQDoxSRehTMf2KdA98MHOgWPEfvQV2YgRI+Tliud4MK7zCry/1ZXTgxTkLzqGxKhy3WaYX0ptrgPRZRaiIBdqbK4DlPMTWsk2QM85XgLHqK4dNcNpdeAY9Zw/Ras6Y9wcLxfWHbik0dTWLUP6dQ12T+lzAL2orTNGUHtOoTbXwZ4n6CGUPiH0das7Zw5Ih3M9rft1Q89HoW7dF7bZu1yo9V+r48wM88uZOtc6rTtc7X+48FIsIiNY4UknQax7zJgxmi4vslhhuzWOUdhci0tVG6A1HmcascJ+tEKMKlmkXw+GpvU/kuo1t0WrlRu4brWsEKNKQeZauPpvhRjVHo8abIsh53864uAYUZhE7m2VwJw5c8IaR7EQHvls7G0s5s2xB2+r1IYVbr8I7bbKQuFqAyx3W6VpH0tv3jqjVrC3Veovkm6rDE746n9ZbqsMD+veVumb9W6rDA8r9OvK9LmtMtT6b+b2MVjmybU3vW6rNOw7oE44OEbasUJPbzUKPYIZfrAo3bAmJiYG/lCYejcrdaKBWHZTTJoEMw/gWTfZhVS1ASGw0u4x82BtsMx8oh8sI7ZEcbDGtAdA2Qcj9K7/wTPJPjZprss0GGHabTFnXGZRpv0TZK7DVf9N8PUn8mhQr83X/oeGg2OknQD1ywqNWvhiDP1yV/3vV1e/7mXLlil+VlYuTKN6yqvxfjFscxOEsN3Kn9RhP6qOUeUxYdLbLwJNyK3nHC8B62uQx63+gluRrA3QY46XIF5VU07PeT/KNgeev1zrnPQQ+hnztaNG1hlvinNgqd7f4Z5rTt0cT0oxep8DhCL0/sg0Z5yhHLfaRiJTpjmeFF4M2xxvobRR4ZoXMaRce2LUXpm2WzHXnuV5C1f9D3jcFpcz7vtRoHJhizGM0/dom3/jcXCMIpBJTooEYtz95sw1kRlE2pwT2mEbJQwzXNZtENZ/IguwwpyDZDmRln8OjpF2xD0vFE7py7RHjx5tUCTelK8QNudl92rn81LzaniYcz/K+Y5Rvr/Duy1lu63SnPtbaVv0bgNC2xPmynUktVHmY654Q5sDK9zbUpbbKgtfM6b++4vXXMdBYOFuo/wzdA68IMubuY0ywzlcOPZPuL4DmPlrphlybRQzfQfUAgfHiIxghV+Yg3hM9m+//aZvLD7XrcXtF2VZXjC0XreBMZr61MTDAttiaK7Vrjq449aQNiCS9qPBU1+rK2aBGK3Q1hu2POhwq40O9V/zfr3MkYSRFdooIwnYRulCn3UbUv8t8dTfSOqPfDPsO6BOODhGgjB+9F7trzeBmrOwzfGgwNeTTqpWrWpANMqMfFqVWmojNMMTzAL/wmzkE6PUPZnQyCdqqn2intonRurhQgrLciVjIb3bAL2esqQHMx+PHmY+HtUK9pjQfx4ahXVG0nHr5+1wnQMEzKChVzypPQ7V9Vt6UP1EzeL3zfsQG1P065I25/V6UltnQokx1Pqv9ni0Qjtqhu+bvuhVZ8z0HVALHBwjQRh/iq/2xFz1wEnZQykzX5NY16tXz0e58FM7eBi2SVoV162ynI6Thqtfd6ByBu5HletWOwGqPr/dqZto199kt0rL05Laid/9vVuyDdCjbmldF/Ss/1ofj/pM0uyf+uPRU87INir89Vot1TEa2NZ7hPLwhdLnAHoJWBdUtmV60PrcQ4+LSdTXGan4b2qWpwfN2lEdzkeLc2jT6Lxex/qvft0Bivk53wq1/mv1UIULD1Uy73Bk8fFowBckvfrrcLX/4cLBMYo8Jer8nj178Mwzz6Bn10646pL6aN/+Stx092PY+s8u4+IDYIbBusDUx7hy5Uod47A4K9yyYIUYKcyCOyaMaQN43GrCCvWfMWpEnxi1rf9W2I8UVlaoWwLHKF79t0CMYTweI+07IAfHKKJ98skn+Pjjj9H28isw9sVJuP/+kfjvQDI6Jw7DL2t+D2ssWt1WaSRfl+SPHTvWgGiUWem2ylBuY9Ebb6vURiTeVqm0H/VuAyLq9jSV5fRk5uNRLSscE1aI0SOU/kj/+q+uHeVtlQHWbYHbKi3Vrwe4rVLteZSewnFbZaj1PyJuq1R5HmUkveqMmb4DaoGDYxTRhgwZgiNHjmDae//DwKHD8dBDo7B52edIqFIJE16bblxgIUzmGLYfA4JY0fDhw9UssMyhhMykk7SW6dJmXTZF5UKtMLm4FSZzVcukE84qHbfq2gCNMddardzAdasVeq6NvGVRzsDjVutlFi1P0/qvEKOkFLcV6rWRudZceGJUzLVp6q4/Ks/XrbApQQapd/03HcX2SGW5sAnfug05/9MRB8dIJ/4r5aLvf4GtXnus3fin13sffvgh2tavin17dmH4E+NRodnVOJxyHDfffDMqVKiAevXqYcaMGQCAfbt3YuTgfqjSrBMu7tgX8xf/KFvWFVdcgQoVKsheq5ZQBdd0uhy79yaFuI3GMtOJ/tdff13qlUK6xxhCx6M894Y5OzLz/g51ge4xhuEkI3znMaEft+ZRGFDJNsDIOQdVC1eMIbVR6uZZCR9/gxZmidGbcgrMGa956kxwg4ylzwGMFqhfVx6AMZfwPXwphDZKg2VoSyHXRf83TzvqzRIx+pkXzZj67y/X5qU8n595866G2dr/UHFwjAxx0/VdUaF8OXy97Cev9xYsWIAmzS9Bs0taAgBcbjf63PUo6tevjzfeeAMNGzbEI488gtmzZ+Ohu29Hq8vaYcq4J1CxQnnc8/hLOHgwOeD6T6SeRvVqCVpvlrASExONDsGSTH2JOJEPSsct2wAiceld/428fZ/CK5JyHUGb4hf7f5h7RE5nkZb/KKMDoDL68VngxHajowAAVKh2KdD+hVKv+m8l4uPjkHhDNyxavgrvulxwOBwAgBMnTmDt2rUYNebZ4rK5uXm469a+eO7V9wAAd955J+rWrYv77rsPr73/CXr3uxWX2g6jd7eOuKT7rZjzxVxMmDTZ57p/3/wXNv75L1546rGybbAmrPArgdoYbVi2bJmukYSPDr2b6l+oDexZLfAremSxwllUcMetMW0Aj1tNCNlGWaEO6kGfXGtb/5nr8LLA/hGyjbIOY+q/kcetBdqoMB6PkfMdsBAHx6zqxHbg0DqjowAARLncCq8GrpSD+92IL5eswJo1a3D99dcDABYtWgS3241e/QbIyo6888K/q1SpghYtWiApKQm9Ei+83qJpQ1SpXBEHDh70uc60tDTcOfp5NGpQD888NipgjFoKPLG5p5z/RtLI7ld5wlkJ48aNw5QpU8Iej7+9UbarssKzd5V/TTTXiZXaCVDDdnoiST5/hlVbt4wQbK6NuZpQZa4VN6ZwW8LVBqidNNw8V2X6y7VxQsm1WQT70AD5JoV3Pi/zHI/etMi1aeq/j09pH4h3f6R6snsDJ2m3Rn/kWbd/hp5HBZlD09R/hTZKi8nuQ6//ZXkYiLn6I4+yRRXe7x5ar82474D64OCYVdVuY3QExZzVLi3T53r36ILKlSpgwYIFxYNjCxYsQLt27dCwcdPicnFxsahRrarss5UrV8ZFF13kdVJXuWIFZGScUVxfdvZ5jBp2H86dP491c2ehQoXyCqX0a6ACdTv+7uf38YlQwikTX3NgDBkyRF4u6G3Rntp1G3HKEuwPOkbsR0+uA63bDHOOqK1bgegxx4vWudZnf6vMtZ+NKdkG6Dl/SuAYVZZTuTw9ad8nBLPusudaVq54eeGndt3hm7vJN62OWz0EO/+WUoylzwH0or5e+6fnsw7U51rd8rSkeX+k47yImvXresSoMoceWh0TelB7ruehFGPo9V/d/gm4FFN89/DP2Lbes25thav9DxcOjllVn9eMjqBY1rlcIDM36M/Fxsagf69rsXjxYsycORMnT57E+vXrvUafHXblqfE8t2KWpnQinJ+fjyfvvwd79uzBynnvo/UlTRU+GUZWeMpaEOvevn072rQJMGBrhjM9QwgYoy55scJ+jCBB5lBVG6A1K9R/K8RoiboVQTFapF8Phqb13wp1xs+VzPJy+ocSNoI8ea/sIihGI/t/q9T/SKHBthhy/qcjTshPhhrc70akpaVh1apVWLhwISRJwuDBgzVdh9vtxmOjRmDL+rV477130P2qKzRdvlpa3VZppAuXX5uYFfaj6kubjdsG091W6WfdVritUu1tbFa4rdKQ24FUxwhV5fSkvq03jvrbKk3cjhb938z9kRmOx0CK63WAHWmOtt7E/ZGFbqsMeDwaeNwG268bUv9VrtvYfh2q1q3FbZWhM3+/HogVvh+Z4dzDCnjlGBmq5zUdkZCQgAULFmD37t3o2LEjGjVqhH+PntFsHY8++iiWfrsIL742HX379gFwWrNl684ij7pR9YuBRbaFNMBcCyeSfjUk0lUEto+s/z5EUqoj8LglH4LMNeu/hWlQryMt/7xyjAxwYcw6Ojoat956KxYuXIjNmzdrdNXYheW//fbbmDlzJq7s2Alx8fH45ptvMfeb5YV/FnyL8+fPKy5B/3vBy35p84X5E/SmPsYvv/yy1CeLYrTJy2mv7POzKd73b+Stn4rllOaCMPL2VN/lAu3HYOe1UViA2oJlLhe2K+VD2BY953gJtG7lYoXlSrYBpotRbf3XhRZ1S2chtFFh248h1RmF90zW1ivP+2Ngn+lnW+THo3f9D10Y+qMgIwpm3WVbkQVutfWzH03dRmkcSjDr9lFQ4ZUw9Zk6tfWG1H/V58x60OLcQ+FFLYVx+h5t8288XjlGhhs8eDA++eQT2Gw2DBo0SNNlb9u2DQCwdctmbN2y2ev9g71uQfnyShPzUzAi6SklRBQ8tgFE4mL9JxIX67/YIi3/vHKMDNezZ09IkgS3242LLrpI9t7stycia996r8+sWbMGO3bs8Ho9efNyfL/k2wufnz0bkiTh+Jkc/HMkA0ePHoKU8lfhn4zDaNiwoebbIxybDYmJiUZHEYDK3zXNMDlV4IK6hmHedatlgf1oiVyrVLQt5m4DLLAfrRCjJW6rskBbr5p12ihj6j9zrc2qrbB/VLLEtlghxuAYUv8jKdeW2BbfzH3+FzwOjhEZwQpPOgniktxly5bpG4ufdassqPHygqH1us11e2r46H9bZdgYmmuNV120LYa0AZbYjxaI0QpPWdS6XlviibpWuNVGh/pvhX5d82VaoY0ykoBtlC70Wbch9d8ST/2NpP7IN8O+A+qEg2MUMtXj3Ur3WVPQLszxYJ4TmtK3wyrO8WIyYZvjIQSKcxOYVKAYzXD+beZcqxW2OV6CVLINsMJevlD/zSt886KFwkrtqPljNDN/5x5aT4kRqkC5DnkOzDCwQIiWqltWiNHMvae/PtMs9d8K58xW+O4RLLPkXyscHCMyNWv8wjR79mxNlxdRrHSGq90CNV4ehV9wOVTXBmiNx5kmhGyjjGSNfj0Y2tZ/5tqcLHAls5EEbqPEq/8WiDGMV60bc/6nHw6OEYWJFOC3DFvR+2Zucn3dFj9t2rTwBlLM994KtL+NZN7ISrB5/UW5WLg2xk9HHzjXxu3xYNdsxHHryWGgtsdfrsPVBgSM0dOOSmapZeZso8zcPqp14bg1ybZY9MuxFnvPNPU/fB2Sn3cCnesZx6a4dn/bYiQz78dCpml71FJoo7RoR0Ot/4H7desw8zFx4XumtjEa9x1QHxwcMzkrXPrtYZ1I1TN+m8zZyJZuWHv16qXiU+HZFnPuMWVm7kRDE6ZcW2j3mfkLU/C8o1XXBoSwRkvtIHU/hFhB5LZR4aGca3Pu01ByzfrvizkDL9tghDn7dbZR/pVt/wT3Gb3rv4fx38l8E7mNClf+w4WDYyZmt9vhcrksNUDmj/m2wjuikq9IkgSXywWbK1+HVZf9ctewzZ8QRIwpKSmKH5XHaK7JhZU/aq7bXRTneDHpQwMC5Tr0qDWOUfUxoYfQj1vz1P9CJdsA08Vohf2oFKPp5kXztx/Dv261xZT3o5ETIBvY1mt97lH019LnACHRYELuQLkOec9qcMuSLEY/9V93oWxL2ObzCqVuhWuOp1Da+kKmaUeD/O5hSP1X247qQfNzD3P1R8HSNP8mwMGxIOXl5eH//u//ULduXcTHx6NTp074+eefdVlXbGwsXC4XUlNTI2aAzCoktwvHswBX9llUSN9udDiml5GRYXQIRGQgtgFE4mL9JxIX67/YIi3/UUYHYDXDhw/HokWL8MQTT6BZs2aYPXs2+vbti9WrV6Nr166arqtWrVrIy8tDeno6MjMz4XA4wjifgnp5BS44C1w4CycO2NyFL549B2QckJVzuyU4cwvgAi6UA4BceTkAcGYXXq2lttwhmxN2z+h39lHA7pCVyy2K8YzNiTx4YjwLpMuX6XK7kZedD1dBAey5x1Du5F+oemxNgD1A3bp1MzoEIjIQ2wAicbH+E4mL9V9skZZ/XjkWhC1btuCrr77Cq6++iqlTp+KBBx7Ar7/+iosvvhjPPPOM5uuz2+1o0KABqlSpgpiYGFMOjAHA0YwcrE9Kw3/7k4ADawr/pO7yKpfncmN9Uho2JZ28UO7AGsVlrk9Kw/qkNNXl8vevv1AuP8ur3KHT2ViflIakpP8ulEv7zzvGAjfW7kvHr3tSUW/bdDT4dxqinOdV7IVgWeFKQPUxzpgxQ8c4/LHAfrTCVZ+MURsa3A5kHsHFaEwbEHn70RBWOB4Zo6lpW/+tsB+tEKNaFtgWK9QtgWM07juAUcTNtZJIyz+vHAvCokWL4HA48MADDxS/FhcXhxEjRmDcuHE4cuQI6tevr+k67XY76tSpo+kytfbL0f2YtukIbrFvxm0xMwtfrN8JuPpWwBFdXO54Zg6mfbYXFZGNh+JevrCAXqOAmPKyZU77aDcA4NGS5XreB8RVkc16+NbHuyFJwMDYt1DHll744qXtgIZtAfuFsd8l+/fi3U1HMdSxDrdEzyp8scn1wFX9ZDEePp2N9+fsQzVkYlzc1gvrzs8GYsoF3hk5Z4C4yoozM8omxcw6CSQ0kcWo+MSYM0cAV4EsRp8KcoDo+JBidEs22G1FDWrWKaDKxbJyF/5a4rOZRzD9zXcCr9ftVB9jbmZhjIGcPwVUrFMqRoWnfmYeBVxOwBGgycvNBApygeg4FTGeBeIqBS6XnQaUS1ARY4q6GM8e1SfG2IryGIv+L4vx7DHA7ZJdmak44XT6QcCZB0TFBl533rnCdQeM8TRQqa7i8SirM+cUYlT6XSEjmBizgNgKKmJM95lrmXPHfcYo298ZyYAzH4iK0TbG+Ko+cl1yPx4H3G55G1VcrmSMhwBnPqZPnx543fnnvdr60GI86R2j0n48c1h9O6o2xpwMr/7Iw6utr3GpirY+mBi16I9KyDoJJDRWsR916I+CaeurNCh1TCg8eSvziLp21JWvU39UW76/lXKttj/KSde+rT+f5l23FGNU2R9lHgYKctXVf7Vt/fk0IKZC4Fwr9kcK9OqP1Jx7KMWo2B8l69Mfqcn1uRMK7ajC/g5TfwSlXCv1R4oxHlIfYyj9kdJ+zPLXH+nd1vvrj0rIOgnUuCRwvx5MW5+fra7+q/5+lApUbaSYa+j9/UhtW591CqhcP/A5s9q23pkbRFsfRH9UoaaKtl5ljH6oyr+F2CROZqXaDTfcgJSUFOzaJb8qatWqVejZsye+++47JCYmKn42NTUVp06dkr2WlJSE/v37Y8eOHWjVqpVucevtj5c6oIXtCCrZcmSvuyUbchArq4QSCichrGDLlZXNlmLhLnEho+egrFhqmblSNJwlxnR9lSuQHMhDTMByesSYI8XABUfAcmpjdEk25EDeYEoA7HCjvC1P9vp5Kc5rW0KJMV+KQj6iA5azQoxOyY5cyE88Q41R7TERaowOuFFO5xjzpGgUqKhbVojRV92yQowxcCLWVlD8uq82KpQYQ21HyyP3wiB6UYzZiAUEa+utECP7I3PGGGn9UZYUh9L1P5QY2R+ZI8YYFCDW5ix+3Yz9Edv6Qnq09bkqY1Rb/9kfmTNGX+0oAKQkzsMlV14PfxITE7Fs2TK/ZYy0c+dOtG7dWvV4C68cC8Lx48cVr+LyvHbs2DGfn505cyYmTpyo+N66deuQlpaGLVu2YMSIERgzZgzmzJlTfLCNGTMGo0ePxm+//YaqVauiXr16WLlyJcaOHYvhw4fj66+/Li47btw4DBkyBNu3F04i36ZNG3z55ZeYMmVKcZlBgwZh9uzZmDZtGnr16oWUlBRkZGSgW7dumDFjBqZPn15cdtiwYZg+fTo+/fRTdOzYETk5Odi/fz/69++PSZMm4YMPPkB5KQeV7Dle22W3SSiPXK/XlZQ+QfAlzlYAoCBguWibC9Hwjqk0PWKMt6l7uqXaGB02CRVUlAOA8jZ126I2xhibEzFwBixnhRijbG7NY1R7TFghxlhbAWJV1C0rxKi2bgHmj9Eq7WgFC8Rohf3I/sg39kf+Gdkflf6y7Av7I//MHqOo7agVYtSjrVcbo9r6z/7IP1P2Ry5XwPGIBQsWYNCgQYaPR3jKjho1Ci+++CKWLFmCJk2a4MSJE6r2lwevHAtCkyZN0KJFC/zwww+y1w8cOIAmTZpg+vTpeOKJJxQ/G8lXjm364iX8t3s7KlWsiG5ROyC53fg34UZEufMR45ZXtozz+Th9Ph9dqmSg7rnt+LP6Lci3l0O8K1NWLq/AjQNpWWhd3Y72GSuxvVovpMVdjPIF6bLH30qShL0ns1Cvciyuyf4ZR8q3QXKFy1HOlQm7JG8QDp/ORkyUHd3s/8IFO7ZXvR7R7jxEu+WNUXpWPjKy89Gl8mnUztqFrTUGwGmL9Yoxt8CFg2nncVl1oG3GL/i3Wh+kx13kM8aLqsSia9ZPOFyxLQ6Vb6cY46HT5xEX7UA3/I18Wyx2Vr1WMca0rDxk5hSga6VTqJH1H/6s0R9OWwziXWflMea7cPD0ebSrLqFNxir8U70vMmLqobxTHqPbXRjjxQmxuPrcSiRXvAKHy7dBOecZ2OGSLTM57TzKxTjQDX8i114eu6r0wOF9O3FJo3ryGM/l4WxuAa6peBIJ2fuxtfoASDYb4lzyOeGy8104fPo8Lq/uRquMX7Gt+s3IjKmNcs4MhRjPoWFCLK4+twL7K3XC0fKtUK7AO8aDaedRMTYK17i34LyjCnZX6YZYVzaiJPlJRurZXGTnu9C1QgqqZh/C1hoDAACxpWI8n+fEkfQctK/uRMsza/BX9USci67pFaOrKMYmCbG46uyPSKrcBSnlL/WOUQIOpGWhUnw0ujs34mx0Deyp3BWxrvOIkuSd3smzucjJd6Fb+SOolJuCrdVvgU0hxqxcJ1LO5ODKavlofuY3/FX9FmRFV/eK0emSsC/1HJpWi0XnzB+xr0pXHItvgXLODNhR4gEYErD/VBaqlotBN+d6ZMTUxt5KXRRj/O/IScSXq4hr4pNRPj8Vf1brB7vkQqxbPl/fudwCHD+Tiyur5aLZmQ34s3o/nI9OQHlnhqycJ8bm1aLRKXMF9lTphhPlmqNcgXeMSaeyUK18NLrl/47TsQ2wr1JnxLnOwSHJT2SPncmByy3hmrj9iM9Px1/VEuGQnIhxZ8vKnc0pwImzuehYNRuNz27Gn9VvQU5UZZRznikVoxv7TmbhkurR6JD5I3ZXvQ4n45soxrgv9RxqVIxFt9w1SI1vjP0VOyrGmJKRAwlAt5j/EO08j20JfeGQCrxizMwuQOq5XHSqmoWLz27Fn9X7IzeqoleMBU43klKzcGl1B67MXIldVa9HanxjxTZqX2oWalWMRbfcX3EivhkOVOygGOPRjBzYbUC3qN2wu/PwT9XecEj5OHZgNxo2bFhc7kx2AdLO5aJz1bOof/ZvbK0xAHmOcijnlLej+U439p/KQqvqDlyRsQI7Em7EqfiGyjGezEKdyrG4JnsVjpW/FAcqXIF411mvGI+kZyPKbpP1R0rtaHF/VDkDdbO2Y2v1/iiwx/vtjy4/8xN2JNyoYX/0D1xw+OyPTmfl40x2Pq6unIZaWbuL+iOFtr64P7KhXcZP+KdaX5/90X8ns1A/2P7IHoedVa5FtDtXsT86dSYL19fIRPWsvdhaYwBctmivGHPyXUjWoT8qHxuFbu4/kOOogF1VeijGeOpcHrJyneha8TgSsg+o64/OrMa2ajep74/KtVKM8eCp86gYp74/uqZ8CqrkHMIfNQYotvWe/uiK6gW49Mxa9f1RlauRUu6SgP1RZnRN/Ff5auX+KDMXuU4Xril3GBVzj+PP6rfABgnH9++Q1f9g+6Nm1WLQKXOFuv6oYB0yYuv47I9OZOYg3ymhW/xBlMs/FbA/6lAtF03PbMCfNW7B+aiqKvqj7jgR30wxxqRTWahePhrXqOiP3G4J18QlITb/DP6udrPf/qhT1fNodHaLz/7I09Zr3R8BwDUB+qMz2QU4ln4O19TMRYOzfxb1RxW82npPjC2rR+HKMz9iR8INOBXfSEV/1BwHKl4ZoD/aBZu7AP9W7QWH5P3dw9MfXVU1Exed3aaiP7LjioyV2J5wI9IC9ke/IKV8SyRXaK8YY+n+aHvVGxEl+e6Prq6cjjpZO7C1+gAU2ON8fvdoU92OyzNW4t9qvXE6toFXO1q2/mgbnIjCjqo9FdtRX/3Ryf3/yuq/J8a21YHLPN+PYi9SjPG/k1loUCUWV2f9hMMV2+FQ+baKMSannUd8jLr+6GxOAbpWSkX18/uwtXp/uG1RiHOdk5XLyXfhUFF/1DpjFf6pfhMyYur67I8aVo1Bl6yVOFipA46Uax2wP8p2VMTuKt0R48rxauu9+qMat0ICvPujPCcOp2fj8uoutDqzxmd/5HJL2HfyHBomxOHqcz+q6o+6uTcjK6oqdlcu1R9Jxf8BANTvPQb1Gl8Kf4YNG4Y5c+b4LWOkYK8c4+BYEFq3bo1atWph1apVstd37dqFVq1a4YMPPsCDDz6oennBJsvM0tPTkZCQYHQYZBDmX2zMP/EYEBvzLzbmX2zMv9iYf7GZPf/BjrfwaZVBqFOnDo4fP+71uue1unXrhjsk0/j000+NDoEMxPyLjfknHgNiY/7FxvyLjfkXG/MvtkjLPwfHgtCuXTvs3bsXZ8/KbxXYvHlz8fui6tixo9EhkIGYf7Ex/8RjQGzMv9iYf7Ex/2Jj/sUWafnnhPxBGDhwIN5880189NFHeOqppwAAeXl5+Oyzz9CpUyfUr18/qOXl5RXe35uUlKR5rOG2Z88eVK9e3egwyCDMv9iYf+IxIDbmX2zMv9iYf7Ex/2Ize/494yyecZdAODgWhE6dOuH222/Hc889h9TUVDRt2hRz5sxBcnJymS4pPHLkCACgf//+GkdKRERERERERCS2I0eOoH379gHLcUL+IOXm5uLFF1/E3LlzkZGRgcsuuwyTJk1Cr169gl7WmTNnsHbtWtSvXx+xsbE6RBsenqduLlmyBE2bNjU6HAoz5l9szD/xGBAb8y825l9szL/YmH+xWSH/eXl5OHLkCLp3744qVaoELM8rx4IUFxeHqVOnYurUqSEvq0qVKrjllls0iMocmjZtavmnblLZMf9iY/6Jx4DYmH+xMf9iY/7FxvyLzez5V3PFmAcn5CciIiIiIiIiImFxcIyIiIiIiIiIiITFwTEiIiIiIiIiIhIWB8coZDVq1MD48eNRo0YNo0MhAzD/YmP+iceA2Jh/sTH/YmP+xcb8iy0S88+nVRIRERERERERkbB45RgREREREREREQmLg2NERERERERERCQsDo4REREREREREZGwODhGRERERERERETC4uAYEREREREREREJi4NjVGZ5eXn4v//7P9StWxfx8fHo1KkTfv75Z6PDIpX++OMPPPLII2jVqhXKly+PBg0aYNCgQdi7d6+s3PDhw2Gz2bz+XHLJJV7LdLvdeOONN9CoUSPExcXhsssuw5dffqm4/t27d6N3796oUKECEhIScPfdd+PUqVO6bCt5W7NmjWJebTYbNm3aJCu7YcMGdO3aFeXKlUPt2rXx2GOPISsry2uZwbQJapdJ+vBVrz1/UlJSAAA9evRQfL93795ey2T+zSkrKwvjx49H7969kZCQAJvNhtmzZyuWVdsu69HWB7NMUk9N/t1uN2bPno1+/fqhfv36KF++PFq3bo3JkycjNzfXa5m+2o3XXnvNq2xKSgoGDRqEKlWqoFKlSrjllltw4MABxVg//fRTXHrppYiLi0OzZs3w3nvvabIPRKa2/ht9rsf6rw+1+fd3PnDDDTcUl0tOTvZZ7quvvvJaLvNvLLXf9QD2/x5RhqyVIsLw4cOxaNEiPPHEE2jWrBlmz56Nvn37YvXq1ejatavR4VEAr7/+OtavX4/bb78dl112GU6cOIH3338f7du3x6ZNm9C6devisrGxsfjkk09kn69cubLXMp9//nm89tpruP/++9GhQwcsXboUd955J2w2G+64447ickePHkW3bt1QuXJlTJkyBVlZWXjzzTexfft2bNmyBTExMfptOMk89thj6NChg+y1pk2bFv9927ZtuP7663HppZfirbfewtGjR/Hmm29i3759+PHHH2WfU9smBLNM0seDDz6Inj17yl6TJAmjRo1Cw4YNUa9eveLXL7roIrz66quysnXr1vVaJvNvTmlpaXj55ZfRoEEDtG3bFmvWrFEsF0y7rEdbr3aZFBw1+c/Ozsa9996Lzp07Y9SoUahZsyY2btyI8ePHY9WqVfj1119hs9lkn7nhhhtwzz33yF67/PLLZf/OysrCtddei8zMTIwbNw7R0dGYPn06unfvjm3btqFatWrFZT/88EOMGjUKt912G5588kn8/vvveOyxx5CdnY3/+7//026HCEZt/QeMPddj/deH2vx/8cUXXq9t3boV77zzDm688Uav94YMGYK+ffvKXrvqqqtk/2b+jaf2ux77/xIkojLYvHmzBECaOnVq8Ws5OTlSkyZNpKuuusrAyEit9evXS3l5ebLX9u7dK8XGxkpDhw4tfm3YsGFS+fLlAy7v6NGjUnR0tDR69Oji19xut3TNNddIF110keR0Ootff+ihh6T4+Hjp0KFDxa/9/PPPEgDpww8/DGWzSKXVq1dLAKSFCxf6LdenTx+pTp06UmZmZvFrH3/8sQRAWrlyZfFrwbQJapdJ4fX7779LAKRXXnml+LXu3btLrVq1CvhZ5t+8cnNzpePHj0uSJEl//PGHBED67LPPvMqpbZf1aOuDWSYFR03+8/LypPXr13t9duLEiRIA6eeff5a9DkCWK19ef/11CYC0ZcuW4td2794tORwO6bnnnit+LTs7W6pWrZp00003yT4/dOhQqXz58lJ6enrAdZEytfXfyHM91n/9qM2/khEjRkg2m006cuRI8WsHDx706ut9Yf6Np/a7Hvv/Czg4RmXy9NNPSw6HQ/blRpIkacqUKRIA6fDhwwZFRqFq37691L59++J/e06YnE6nV75LmjFjhgRA2rlzp+z1+fPnSwCk33//vfi1mjVrSrfffrvXMpo3by5df/31GmwFBVJycOzs2bNSQUGBV5nMzEwpKipKevrpp2Wv5+XlSRUqVJBGjBhR/JraNiGYZVJ4PfTQQ5LNZpMOHjxY/JpncKygoEA6d+6cz88y/9bg78uR2nZZj7Y+mGVS2QX75fjff/+VAEjvvvuu7HXP4Fh2draUk5Pj8/MdOnSQOnTo4PX6jTfeKDVp0qT438uXL5cASMuXL5eV27BhgwRA+uKLL1TFS/6pGRwz4lyP9T88gqn/ubm5UpUqVaQePXrIXi85OJaVleU18FIS829epb/rsf+/gHOOUZn8/fffaN68OSpVqiR7vWPHjgAKb5sh65EkCSdPnkT16tVlr2dnZ6NSpUqoXLkyEhISMHr0aK/5gf7++2+UL18el156qex1zzHx999/AyicfyQ1NRVXXnml1/o7duxYXI7C495770WlSpUQFxeHa6+9Flu3bi1+b/v27XA6nV65iomJQbt27WS5UtsmBLNMCp+CggJ8/fXX6NKlCxo2bCh7b+/evShfvjwqVqyI2rVr48UXX0RBQYGsDPNvbcG0y3q09WqXSeF14sQJAPA6JwCA2bNno3z58oiPj0fLli0xf/582ftutxv//vuvz/zv378f586dA3Ahv6XLXnHFFbDb7cx/mBh1rsf6bz4//PADzpw5g6FDhyq+P3HiRFSoUAFxcXHo0KEDfvrpJ9n7zL95lf6ux/5fjnOOUZkcP34cderU8Xrd89qxY8fCHRJpYN68eUhJScHLL79c/FqdOnXwzDPPoH379nC73VixYgVmzpyJf/75B2vWrEFUVGEzcvz4cdSqVctrXpLSx8Tx48dlr5cum56ejry8PMTGxuqyjVQoJiYGt912G/r27Yvq1atj165dePPNN3HNNddgw4YNuPzyywPm6vfffy/+t9o2IZhlUvisXLkSp0+f9joRbtKkCa699lq0adMG58+fx6JFizB58mTs3bsXCxYsKC7H/FtbMO2yHm292mVSeL3xxhuoVKkS+vTpI3u9S5cuGDRoEBo1aoRjx45hxowZGDp0KDIzM/HQQw8BQHF+A7ULLVq0wPHjx+FwOFCzZk1ZuZiYGFSrVo35DwMjz/VY/81n3rx5iI2NxcCBA2Wv2+123HjjjRgwYADq1auHAwcO4K233kKfPn3w3Xff4aabbgLA/JtZ6e967P/lODhGZZKTk6M4eBEXF1f8PlnLnj17MHr0aFx11VUYNmxY8eulJ+K+44470Lx5czz//PNYtGhR8USJao8Jz/8DleXgmL66dOmCLl26FP+7X79+GDhwIC677DI899xzWLFiRcBclaznWuWfbYcx5s+fj+joaAwaNEj2+qeffir79913340HHngAH3/8McaMGYPOnTsDYP6tLph2WY+2nucU5jNlyhT88ssvmDlzJqpUqSJ7b/369bJ/33fffbjiiiswbtw4DB8+HPHx8arz7/m/rwfxsF0IDyPP9Vj/zeXs2bNYvnw5+vbt61X3GzRogJUrV8peu/vuu9GyZUuMHTu2eHCM+Tcnpe967P/leFsllUl8fDzy8vK8Xvc88js+Pj7cIVEITpw4gZtuugmVK1fGokWL4HA4/JYfM2YM7HY7fvnll+LX1B4Tnv/z+DGfpk2b4pZbbsHq1avhcrkC5qpknrTKP3MffllZWVi6dCl69eole3qcL2PHjgUAXeo/82+MYNplPdp6nlOYy4IFC/DCCy9gxIgRxVeC+RMTE4NHHnkEZ86cwZ9//gkg+Pzn5+crLpvtgnHCda7H+m8u33zzDXJzc33eUllaQkIC7r33Xvz33384evQoAObfjHx912P/L8fBMSqTOnXqFF8yWZLntbp164Y7JCqjzMxM9OnTB2fOnMGKFStU5S4+Ph7VqlVDenp68Wt16tTBiRMnIEmSrGzpY8Jzmayv4ychIYFXjRmofv36yM/Px/nz5wPmquSxorZNCGaZFB5LlixBdna26hPh+vXrA4BX/Wf+rSuYdlmPtl7tMkl/P//8M+655x7cdNNN+OCDD1R/rnS74Mmv2nbB5XIhNTVVVi4/Px+nT59m/g0SrnM91n9zmTdvHipXroybb75Z9WdK13/m31z8fddj/y/HwTEqk3bt2mHv3r04e/as7PXNmzcXv0/ml5ubi8TEROzduxfff/89WrZsqepz586dQ1paGmrUqFH8Wrt27ZCdnY3du3fLypY+JurVq4caNWrIJn732LJlC48dgx04cABxcXGoUKECWrdujaioKK9c5efnY9u2bbJcqW0Tglkmhce8efNQoUIF9OvXT1X5AwcOAIBX/Wf+rSuYdlmPtl7tMklfmzdvxoABA3DllVfi66+/Lp5nSo3S7YLdbkebNm0U879582Y0btwYFStWBHAhv6XLbt26FW63m/k3SLjO9Vj/zeP48eNYvXo1brvttqB+qC5d/5l/8wj0XY/9vxznHDPQmTNnsHbtWtSvX99yV8q0b98eLpcLL7/8Mu69914AhV9uPvjgA7Rp0wZnz57Fzp07DY6S/HG5XHj88cexYcMGvPfee6hUqZJXzvLy8uB0OlG+fHnZ69OmTYMkSWjZsmXxZy655BJERUVh0qRJeOGFFwAUPhFl2rRpqFmzJipXrlxc9tprr8XSpUvxyy+/FP+6sGnTJuzduxeDBw/msRMG6enpSEhIkL22Z88eLF26FF27di3upDp16oTPPvsMAwcOLD4OvvnmG2RlZaFjx47FuQqmTVC7TNJfeno6fv75Z/Tt2xcHDx6UvZeVlYWYmBjZXECSJOHpp58GUHgbLvNvLfv37wdQ+CSp0vtZbbusR1sfzDKp7Pzlf//+/bjnnntQu3ZtTJ06tfjLbmlKfcf58+fxxhtvoEqVKoiPjy9e9jXXXIPp06djwYIFaN26NQDg4MGDWLVqFYYPH15crnbt2qhUqRLeeOMNXHzxxcXLffXVVxEXF4cmTZow/xrwlX+jz/VY/8PDX/33+Pzzz+F2u3H11VcrllGq/ydPnsTHH3+M5s2bIz09vfjqMebfeGq+6wGR3f/n5eXhyJEj6N69u9ccekpsUulr2CLYH3/8gTlz5mD16tVITk5GtWrV0LlzZ0yePBnNmzf3+9njx4/jnXfewebNm7F161ZkZWVh9erV6NGjR5njWbp0Kfr371/mzxMRERERERERkbIlS5bglltuCVhOqCvHXn/9daxfvx633347LrvsMpw4cQLvv/8+2rdvj02bNhX/qqXkv//+w+uvv45mzZqhTZs22LhxY8jxeO7PXrJkCZo2bRry8oy0fft2tGnTxugwyCDMv9iYf+IxIDbmX2zMv9iYf7Ex/2Ize/6TkpLQv3//4nGXQIQaHHvyyScxf/582S0igwcPRps2bfDaa69h7ty5Pj97xRVX4PTp00hISMCiRYtw++23hxyP51bKpk2bolWrViEvz0h79+61/DZQ2TH/YmP+iceA2Jh/sTH/YmP+xcb8i80q+Vc7hZVQg2NdunTxeq1Zs2Zo1aqV1yRwpXkmDSVlGRkZRodABmL+xcb8E48BsTH/YmP+xcb8i435F1uk5V+owTElkiTh5MmTuo94pqam4tSpU7LXkpKSdF1nOHXr1s3oEMhAzL/YmH/iMSA25l9szL/YmH+xMf9ii7T8240OwGjz5s1DSkoKBg8erOt6Zs6cidatW8v+eCbjX7duHdauXYupU6ciPT0dw4YNAwAkJiYCAMaMGYOkpCTMmjULixcvxpYtWzBp0iRkZ2dj0KBBsrLjxo3D9u3bMX/+fMyfPx/bt2/HuHHjZGUGDRqE7OxsTJo0CVu2bMHixYsxa9YsJCUlYcyYMbKyw4YNQ3p6OqZOnYq1a9dixYoVmDFjBlJSUjBq1KjisjNmzMCoUaOQkpKCGTNmYMWKFZbfJgDcJpXbNGPGjIjbpkjMk17bNG3atIjbpkjMk57b9PDDD0fcNkVinvTapjFjxkTcNkVinvTapueeey7itikS86TXNk2cODHitikS86TXNr3++usRt02RmCe9tuntt9829Tb98ccfCIZQT6ssbc+ePejUqRNatWqF33//HQ6HQ9XnPHOOBfO0Sl9XjvXv3x87duywxL26RERERERERERmt3PnTrRu3Vr1eIuwt1WeOHECN910EypXroxFixapHhgrq5o1a6JmzZq6rsNIiYmJWLZsmdFhkEGYf7Ex/8RjQGzMv9iYf7Ex/2Jj/gGn04mMjAxkZWVBtOuO1q1bh65du4Z1nTabDRUqVEDVqlURFaXtcJaQV45lZmaiR48eOHz4MH7//Xe0bNkyqM+X5coxJcGOZBIRERERERGR8SRJwqFDh5CTkwOHw6H5YA15czqdcLlcKFeuHBo0aACbzeazLK8cCyA3NxeJiYnYu3cvfvnll6AHxkjZsGHDMGfOHKPDIIMw/2Jj/onHgNiYf7Ex/2Jj/sUmev7PnTuHnJwcVK5cGXXq1PE7UBOJDh48iEaNGoV1nZIk4fjx48jMzMS5c+dQqVIlzZYt1IT8LpcLgwcPxsaNG7Fw4UJcddVViuWOHz+OPXv2oKCgIMwRWtf06dONDoEMxPyLjfknHgNiY/7FxvyLjfkXm+j5P3v2LIDCKZREGxgDgPr164d9nTabrXi6Ks/+14pQg2Njx47Fd999hz59+iA9PR1z586V/fF47rnncOmllyIlJUX2+cmTJ2Py5Mn45ptvAABffPFF8Wui+/TTT40OgQzE/IuN+SceA2Jj/sXG/IuN+Reb6PkvKChAVFSUsLdTpqWlGbJezz7X+mImobK4bds2AMCyZcsUJw686667/H7+xRdflP171qxZxX9/4YUXQg/Qwjp27Gh0CGQg5l9szD/xGBAb8y825l9szL/YRM+/JEmw24W63kimfPnyhq3bbrdr/gAEoTK5Zs0aSJLk84/H7NmzIUkSGjZsKPu8ms+KKicnx+gQyEDMv9iYf+IxIDbmX2zMv9iYf7Ex/xDydkoPt9tt2Lr12O9CDY6Rfvbv3290CGQg5l9szD/xGBAb8y825l9szL/YmH+x5eXlGR2Cpjg4Rpro37+/0SGQgZh/sTH/xGNAbMy/2Jh/sTH/YmP+xValShWjQ9AUB8dIE5MmTTI6BDIQ8y825p94DIiN+Rcb8y825l9szL/Yjh8/bnQImrJJnDDLMDt37kTr1q2xY8cOtGrVyuhwiIiIiIiIiEiFAwcOAAAaN25scCTiUbPvgx1v4ZVjpInExESjQyADMf9iY/6Jx4DYmH+xMf9iY/7FxvyLbd++fUaHoCleOWYgXjlGREREREREZD28csyb2+1Gfn4+4uLidF0Prxwj0xo1apTRIZCBmH+xMf/EY0BszL/YmH+xMf9iY/4j14QJE2Cz2bBnzx4MGjQIlSpVQrVq1fD4448jNzcXAHDo0CHYbDY88sgjmDdvHlq1aoXY2FisWLECAJCSkoL77rsPtWrVQmxsLFq1aoVZs2YZuVl+RRkdAEWGF1980egQyEDMv9iYf+IxIDbmX2zMv9iYf7Ex/5Fv0KBBaNiwIV599VVs2rQJ7777LjIyMvD555+jTp06AIBff/0VX3/9NR555BFUr14dDRs2xMmTJ9G5c+fiwbMaNWrgxx9/xIgRI3D27Fk88cQTxm6YAg6OkSaWLFmC0aNHGx0GGYT5FxvzTzwGxMb8i435FxvzLzbmX9nEZTux69hZo8Mo1rJuJYxPLNs0To0aNcLSpUsBAKNHj0alSpUwc+ZMPPXUU6hduzYA4L///sP27dvRsmXL4s+NHDkSLpcL27dvR7Vq1QAUXmk4ZMgQTJgwAQ8++CDi4+ND3DJtcXCMNNGkSROjQyADMf9iY/6Jx4DYmH+xMf9iY/7Fxvwr23XsLDYfTDc6DE2UHvx89NFHMXPmTPzwww946KGHAADdu3eXDYxJkoRvvvkGgwYNgiRJSEtLK36vV69e+Oqrr/DXX3/h6quvDs9GqMTBMdKE2UZ9KbyYf7Ex/8RjQGzMv9iYf7Ex/2Jj/pW1rFvJ6BBkQomnWbNmsn83adIEdrsdycnJsNsLp7Bv1KiRrMypU6dw5swZfPTRR/joo48Ul5uamlrmmPTCwTHSxJYtW9C9e3ejwyCDMP9iY/6Jx4DYmH+xMf9iY/7FxvwrK+stjFZgs9mK/37+/HkA3oOkbrcbAHDXXXdh2LBhisu57LLLdIqw7Dg4RpoYMWKE0SGQgZh/sTH/xGNAbMy/2Jh/sTH/YmP+I9++fftkV4YlJSXB7XajYcOGqF69uuJnatSogYoVK8LlcqFnz57hCjVkdqMDoMgwZswYo0MgAzH/YmP+iceA2Jh/sTH/YmP+xcb8R74ZM2bI/v3ee+8BAPr06YMjR44ofsbhcOC2227DN998gx07dni9f+rUKe0D1QCvHCNNzJkzx+gQyEDMv9iYf+IxIDbmX2zMv9iYf7Ex/5Hv4MGD6NevH3r37o2NGzdi7ty5uPPOO9G2bVu/n3vttdewevVqdOrUCffffz9atmyJ9PR0/PXXX/jll1+Qnm6+BxbwyjHSRGJiotEhkIGYf7Ex/8RjQGzMv9iYf7Ex/2Jj/iPfggULEBsbi2effRbLly/HI488gk8//RRA4S2XvtSqVQtbtmzBvffei2+//RaPPPII3nnnHaSnp+P1118PV/hBsUmSJBkdhKh27tyJ1q1bY8eOHWjVKnIn7SMiIiIiIiKKJAcOHAAANG7c2OBItDdhwgRMnDgRp06d8jm3mJHU7Ptgx1t45Rhpgvebi435FxvzTzwGxMb8i435FxvzLzbmX2y+5hyzKg6OkSZGjx5tdAhkIOZfbMw/8RgQG/MvNuZfbMy/2Jh/sdWoUcPoEDTFwTHSxG+//WZ0CGQg5l9szD/xGBAb8y825l9szL/YmH+xZWVlGR2Cpjg4RpqoWrWq0SGQgZh/sTH/xGNAbMy/2Jh/sTH/YmP+I9eECRMgSZLf+cYcDkcYI9IfB8dIE/Xq1TM6BDIQ8y825p94DIiN+Rcb8y825l9szL/YYmJijA5BUxwcI02sXLnS6BDIQMy/2Jh/4jEgNuZfbMy/2Jh/sTH/YsvMzDQ6BE3ZJEmSjA5CVME+WtTMsrOzUa5cOaPDIIMw/2Jj/onHgNiYf7Ex/2Jj/sUmev4PHDgAAGjcuLHBkRjD5XIZdmulmn0f7HgLrxwjTQwfPtzoEMhAzL/YmH/iMSA25l9szL/YmH+xMf9iS05ONjoETfHKMQNF0pVjRERERERERKIQ/coxI/HKMTKtxMREo0MgAzH/YmP+iceA2Jh/sTH/YmP+xcb8i23fvn1Gh6ApXjlmIF45RkRERERERGQ9vHLMOLxyjExr3LhxRodABmL+xcb8E48BsTH/YmP+xcb8i435F9vRo0eNDkFTHBwjTQwZMsToEMhAzL/YmH/iMSA25l9szL/YmH+xMf9iS0hI0GxZ2dnZmDBhAtasWaPZMoPFwTHSxPbt240OgQzE/IuN+SceA2Jj/sXG/IuN+Rcb8y+2nJwczZaVnZ2NiRMncnCMiIiIiIiIiIjICBwcI020adPG6BDIQMy/2Jh/4jEgNuZfbMy/2Jh/sTH/kWvChAmw2WzYs2cPBg0ahEqVKqFatWp4/PHHkZubCwCIj4+H0+nEpEmT0KRJE8TGxqJhw4YYN24c8vLyZMvbunUrevXqherVqyM+Ph6NGjXCfffdBwBITk5GjRo1AAATJ06EzWaDzWbDhAkTwrrNphsc27x5M7766iv8/PPPxTudzO/LL780OgQyEPMvNuafeAyIjfkXG/MvNuZfbMx/5Bs0aBByc3Px6quvom/fvnj33XfxwAMPAADS09MxcuRIvPTSS2jfvj2mT5+O7t2749VXX8Udd9xRvIzU1FTceOONSE5OxrPPPov33nsPQ4cOxaZNmwAANWrUwP/+9z8AwIABA/DFF1/giy++wK233hrWbbVJkiSFdY0+nDt3Dn369MHGjRuLX6tduzaWL1+Odu3aGReYjoJ9tCgRERERERERGe/AgQMAgMaNG8vf+PFZ4ISJ5mOr3Qbo81pQH5kwYQImTpyIfv36YenSpcWvjx49GjNnzsQ///wDSZLQrl07jBw5Eh9//HFxmaeffhpvvvkmfv31V1x77bVYsmQJBgwYgD/++ANXXnml4vrS0tJQo0YNjB8/XtUVYz73fQnBjrdEBSwRJm+88QY2bNiAW2+9Fddddx2SkpLwv//9D8OGDcM///xjdHgUQGJiIpYtW2Z0GGQQ5l9szD/xGBAb8y825l9szL/YmH8fTmwHDq0zOgpNjB49WvbvRx99FDNnzsQPP/yAU6dOAQCefPJJWZmxY8fizTffxPLly3HttdeiSpUqAIDvv/8ebdu2RXR0dFhiD5ZpBse+/fZb3HrrrVi0aFHxa5dccgkeeughHDx4EI0aNTIwOgqEjaLYmH+xMf/EY0BszL/YmH+xMf9iY/59qG2yudhCiKdZs2ayfzdp0gR2ux3JyckAALvdjqZNm8pXV7s2qlSpgkOHDgEAunfvjttuuw0TJ07E9OnT0aNHD/Tv3x933nknYmNjyxyb1kwzOJacnIzHH39c9lqvXr0gSRKOHj3KwTGTGzRoEL7++mujwyCDMP9iY/6Jx4DYmH+xMf9iY/7Fxvz7EOQtjFZis9mK/3727Fmv13x9ZtGiRdi0aROWLVuGlStX4r777sO0adOwadMmVKhQQdeY1TLNhPw5OTleO8Xz74KCAiNCoiDMnj3b6BDIQMy/2Jh/4jEgNuZfbMy/2Jh/sTH/kW/fvn2yfyclJcHtdqNhw4Zo1aoV3G63V5mTJ0/izJkzuPjii2Wvd+7cGa+88gq2bt2KefPmYefOnfjqq68ABB5gCwfTDI4BwPnz55Geni77AxRO1l/6dc97ZA7Tpk0zOgQyEPMvNuafeAyIjfkXG/MvNuZfbMx/5JsxY4bs3++99x4AoE+fPujYsSMA4O2335aVeeuttwAAN910EwAgIyMDpZ8D6XnoYl5eHgCgXLlyAIAzZ85oFnuwTHNbJQCMGjUKo0aN8nrd1yM8XS6X3iGRSr169TI6BDIQ8y825p94DIiN+Rcb8y825l9szH/kO3jwIPr164fevXtj48aNmDt3Lu688060bdsW58+fx7Bhw/DRRx/hzJkz6N69O7Zs2YI5c+agf//+uPbaawEAc+bMwcyZMzFgwAA0adIE586dw8cff4xKlSqhb9++AID4+Hi0bNkSCxYsQPPmzZGQkIDWrVujdevWYdtW0wyOjR8/Xvd1/PHHH5gzZw5Wr16N5ORkVKtWDZ07d8bkyZPRvHnzgJ8/c+YMnnnmGSxevBjZ2dno2LEjpk2bhvbt2+seu9mlpKQYHQIZiPkXG/NPPAbExvyLjfkXG/MvNuY/8i1YsAAvvfQSnn32WURFReGRRx7B1KlTAQD5+fn45JNP0LhxY8yePRuLFy9G7dq18dxzz8nGdzyDZl999RVOnjyJypUro2PHjpg3b55sbvlPPvkEjz76KMaMGYP8/HyMHz+eg2N6ef3117F+/XrcfvvtuOyyy3DixAm8//77aN++PTZt2uR3x7vdbtx00034559/8PTTT6N69eqYOXMmevTogT///NPrKQ6iycjIMDoEMhDzLzbmn3gMiI35FxvzLzbmX2zMf+SrUaMGFi5cqPiey+VCVFQUXnrpJbz00ks+l3H55Zdj/vz5Add11VVXYevWrWWONVSmGRwLxrZt2zB37ly8+eabQX3uySefxPz58xETE1P82uDBg9GmTRu89tprmDt3rs/PLlq0CBs2bMDChQsxcOBAAIVP52jevDnGjx+vKtmRrFu3bkaHQAZi/sXG/BOPAbEx/2Jj/sXG/IuN+RebWZ4yqRXLDI4lJydj/vz5mDdvHnbv3g2bzRb04FiXLl28XmvWrBlatWqF3bt3+/3sokWLUKtWLdn8ZzVq1MCgQYMwd+5c5OXlITY2Nqh4IkV6ago+nvk2nn7qaaNDIYMw/2Jj/onHgNiYf7Ex/2Jj/sUmev7zcs4jOjYOBfl5RoeiudIT6Cs5deoU6tevH4ZowsMmqdlqg5w+fRpff/015s2bh40bNyI6Ohrdu3dHv379kJiYiAYNGoS8DkmSUL9+fbRq1QorV670Wa5Zs2Zo1qwZfvjhB9nrn376KUaOHIl///0Xbdq08fn51NRUnDp1SvZaUlIS+vfvjx07dqBVq1ahbYiB9k9qhyaug0aHQURERERERBQWB654CWjcA42r2o0ORXPPv78AU159HadOnUL16tWNDsfLgQMHAACNGzf2WWbnzp1o3bq16vEW02UxJycHX331FRITE1G3bl088cQTsNsLw5w7dy5WrlyJ0aNHazIwBgDz5s1DSkoKBg8e7Lfc8ePHUadOHa/XPa8dO3bM7+dnzpxZ/LQFz5/+/fsDANatW4e1a9di6tSpSE9Px7BhwwAAiYmJAIAxY8YgKSkJs2bNwuLFi7FlyxZMmjQJ2dnZGDRokKzsuHHjsH37dsyfPx/z58/H9u3bMW7cOFmZQYMGITs7G5MmTcKWLVuwePFizJo1C0lJSRgzZoys7LBhw5Ceno6pU6di7dq1WLFiBWbMmIGUlJTiJ4vm5+X73XYiIiIiIiIisoYXxj2HvXv3onr16jhy5Ahyc3ORlpaGjIwMnD9/HseOHcO+ffuwf/9+AMC+ffsAAEePHkV2djZOnz6N06dPIzs7G0ePHpWV2b9/P1wuF44dO4bz588jIyMDaWlpyM3NxZEjR2RlDx48CKfTiRMnTuDcuXPIzMxEamoq3G43/vzzTwAXxi5GjRqFlJQUzJgxAytWrMAff/wR1Dab5sqxlStXYt68eViyZAmys7PRo0cP3HHHHbjttttw+vRpNG/eHIsWLZLd1hiqPXv2oFOnTmjVqhV+//13OBwOn2UdDgcefPBBzJw5U/b6r7/+iuuvvx6LFy8uHuxSEslXjv3142coyDptdBhEREREREREYWGv2QqVa12MujUTjA5Fc7EVqyI62rzTRulx5Zhp5hzr06cPGjVqhClTpuD2229HrVq1it9LT0/XfH0nTpzATTfdhMqVK2PRokV+B8YAID4+Hnl53vcS5+bmFr/vT82aNVGzZs2yB2xi7fvci2HDhmHOnDlGh0IGYf7FxvwTjwGxMf9iY/7FxvyLTfT8HzhwAJIkoUJCbaNDMcTBgwfRqFEjQ9YtSRJsNpumyzTNbZW1a9fGwYMHMWfOHMybNy/gbYqhyMzMRJ8+fXDmzBmsWLECdevWDfiZOnXq4Pjx416ve15Ts4xINn36dKNDIAMx/2Jj/onHgNiYf7Ex/2Jj/sUmev7tdjtcLpeqyesjkVGT8UuSBJfLVTz9llZMMzh29OhR/PTTT2jVqhUmTpyIBg0aoFu3bvjggw+8bkcMRW5uLhITE7F37158//33aNmyparPtWvXDn/99Rfcbrfs9c2bN6NcuXJo3ry5ZjFa0aeffmp0CGQg5l9szD/xGBAb8y825l9szL/YRM9/bGwsXC4XUlNThRwgS0tLC/s6JUlCamoqXC4XYmO1ve3TNLdV2u129OzZEz179sQHH3yApUuXYt68eXj88cfhdDphs9nw22+/oVOnTqhXr16Z1uFyuTB48GBs3LgRS5cuxVVXXaVY7vjx48jMzESTJk0QHR0NABg4cCAWLVqEb7/9FgMHDgRQeDAsXLgQiYmJmifGajp27Gh0CGQg5l9szD/xGBAb8y825l9szL/YRM9/rVq1kJeXh/T0dGRmZsLhcGh+q5+ZFRQUIDs7O2zr81wx5nK5EB8fL5uKSwumGRwrKS4uDoMHD8bgwYORnp6Or776CvPnz8e7776L9957D+3atUO/fv0wfvz4oJY7duxYfPfdd0hMTER6ejrmzp0re/+uu+4CADz33HOYM2cODh48iIYNGwIoHBzr3Lkz7r33XuzatQvVq1fHzJkz4XK5MHHiRE2228pycnKMDoEMxPyLjfknHgNiY/7FxvyLjfkXm+j5t9vtaNCgAU6ePIm8vDyvu8wiXVpaGurUqRO29dlsNsTExCA2Nha1atXS/LZKUw6OlZSQkICHH34YDz/8MA4ePIh58+Zh3rx5ePnll4MeHNu2bRsAYNmyZVi2bJnX+57BMSUOhwM//PADnn76abz77rvIyclBhw4dMHv2bLRo0SKoOCKR5xGuJCbmX2zMP/EYEBvzLzbmX2zMv9iY/8IBsnAOEJnJjz/+iKuvvtroMDRjkyx6c+zff/+Nyy+/3OgwQhLso0XNLCUlpcy3u5L1Mf9iY/6Jx4DYmH+xMf9iY/7FxvyLzez5D3a8xRQT8tvtdjgcjqD+dOjQweiwqYRJkyYZHQIZiPkXG/NPPAbExvyLjfkXG/MvNuZfbJGWf1NcOTZhwgSviesWL16MnTt3olevXsW3Le7Zswc//fQTWrdujf79+wd9W6XZRNKVY0REREREREREZmDJK8cmTJiA8ePHF/+pU6cOUlNTsWPHDnz//feYNm0apk2bhuXLl+Pff//FiRMnULduXaPDphISExONDoEMxPyLjfknHgNiY/7FxvyLjfkXG/MvtkjLvymuHCutWbNmuPfeezFu3DjF91955RXMnj0b+/btC3Nk2uKVY0RERERERERE2rLklWOlHT16FNHR0T7fj46OxtGjR8MYEQUyatQoo0MgAzH/YmP+iceA2Jh/sTH/YmP+xcb8iy3S8m/KK8c6dOiAtLQ0rFu3zuvpB0ePHkXXrl1Rs2ZNbNmyxaAItRFJV46Z/UkVpC/mX2zMP/EYEBvzLzbmX2zMv9iYf7GZPf8RceXY9OnTkZqaiubNm+Ouu+7ChAkTMGHCBAwdOhQtWrRAamoq3nrrLaPDpBKWLFlidAhkIOZfbMw/8RgQG/MvNuZfbMy/2Jh/sUVa/qOMDkBJ165dsXnzZrz44otYvHgxcnJyAADx8fHo1asXJk6ciDZt2hgcJZXUpEkTo0MgAzH/YmP+iceA2Jh/sTH/YmP+xcb8iy3S8m/KwTEAaN26NRYvXgy3241Tp04BAGrUqAG73ZQXuwkvPj7e6BDIQMy/2Jh/4jEgNuZfbMy/2Jh/sTH/You0/Jt+pMlut6NWrVqoVasWB8ZMzOrzv1FomH+xMf/EY0BszL/YmH+xMf9iY/7FFmn5N+WE/KKIpAn509PTkZCQYHQYZBDmX2zMP/EYEBvzLzbmX2zMv9iYf7GZPf8RMSE/Wc+YMWOMDoEMxPyLjfknHgNiY/7FxvyLjfkXG/MvtkjLP68cM1AkXTlGRERERERERGQGvHKMDJGYmGh0CGQg5l9szD/xGBAb8y825l9szL/YmH+xRVr+eeWYgXjlGBERERERERGRtnjlGBki0u43puAw/2Jj/onHgNiYf7Ex/2Jj/sXG/Ist0vLPwTHSxOjRo40OgQzE/IuN+SceA2Jj/sXG/IuN+Rcb8y+2SMs/B8dIE7/99pvRIZCBmH+xMf/EY0BszL/YmH+xMf9iY/7FFmn55+AYaaJq1apGh0AGYv7FxvwTjwGxMf9iY/7FxvyLjfkXW6Tln4NjpIl69eoZHQIZiPkXG/NPPAbExvyLjfkXG/MvNuZfbJGWfw6OkSZWrlxpdAhkIOZfbMw/8RgQG/MvNuZfbMy/2Jh/sUVa/m2SJElGByGqYB8tambZ2dkoV66c0WGQQZh/sTH/xGNAbMy/2Jh/sTH/YmP+xWb2/Ac73sIrx0gTw4cPNzoEMhDzLzbmn3gMiI35FxvzLzbmX2zMv9giLf+8csxAkXTlGBERERERERGRGfDKMTJEYmKi0SGQgZh/sTH/xGNAbMy/2Jh/sTH/YmP+xRZp+eeVYwbilWNERERERERERNrilWNkiHHjxhkdAhmI+Rcb8088BsTG/IuN+Rcb8y825l9skZZ/Do6RJoYMGWJ0CGQg5l9szD/xGBAb8y825l9szL/YmH+xRVr+OThGmti+fbvRIZCBmH+xMf/EY0BszL/YmH+xMf9iY/7FFmn55+AYEREREREREREJi4NjpIk2bdoYHQIZiPkXG/NPPAbExvyLjfkXG/MvNuZfbJGWfw6OkSa+/PJLo0MgAzH/YmP+iceA2Jh/sTH/YmP+xcb8iy3S8m+TJEkyOghRBftoUSIiIiIiIiIi8i/Y8RZeOUaaSExMNDoEMhDzLzbmn3gMiI35FxvzLzbmX2zMv9giLf+8csxAvHKMiIiIiIiIiEhbvHKMDDFo0CCjQyADMf9iY/6Jx4DYmH+xMf9iY/7FxvyLLdLyzyvHDBRJV45lZ2ejXLlyRodBBmH+xcb8E48BsTH/YmP+xcb8i435F5vZ888rx8gQ06ZNMzoEMhDzLzbmn3gMiI35FxvzLzbmX2zMv9giLf8cHCNN9OrVy+gQyEDMv9iYf+IxIDbmX2zMv9iYf7Ex/2KLtPxzcIw0kZKSYnQIZCDmX2zMP/EYEBvzLzbmX2zMv9iYf7FFWv45OEaayMjIMDoEMhDzLzbmn3gMiI35FxvzLzbmX2zMv9giLf/CDY5lZWVh/Pjx6N27NxISEmCz2TB79mzVn//555/RtWtXlCtXDlWrVsXAgQORnJysW7xW0a1bN6NDIAMx/2Jj/onHgNiYf7Ex/2Jj/sXG/Ist0vIv3OBYWloaXn75ZezevRtt27YN6rPff/89evfujby8PLz22msYO3Ys1q5di65du+LUqVM6RWwNM2bMMDoEMhDzLzbmn3gMiI35FxvzLzbmX2zMv9giLf82SZIko4MIp7y8PGRkZKB27drYunUrOnTogM8++wzDhw8P+NlWrVohPz8fO3fuRExMDADgn3/+Qfv27fHEE08E/bSGYB8tSkRERERERERE/gU73iLclWOxsbGoXbt20J9LT0/Hrl27MGDAgOKBMQBo27YtLr30Unz11Vdahmk5iYmJRodABmL+xcb8E48BsTH/YmP+xcb8i435F1uk5T/K6ACsIi8vDwAQHx/v9V65cuWwc+dOnDhxwufAW2pqqtetl0lJSdoHapBly5YZHQIZiPkXG/NPPAbExvyLjfkXG/MvNuZfbJGWf+GuHCurWrVqoUqVKli/fr3s9dOnT2PXrl0A/D/KdObMmWjdurXsT//+/QEA69atw9q1azF16lSkp6dj2LBhAC6MxI4ZMwZJSUmYNWsWFi9ejC1btmDSpEnIzs7GoEGDZGXHjRuH7du3Y/78+Zg/fz62b9+OcePGycoMGjQI2dnZmDRpErZs2YLFixdj1qxZSEpKwpgxY2Rlhw0bhvT0dEydOhVr167FihUrMGPGDKSkpGDUqFHFZYcNG4ZRo0YhJSUFM2bMwIoVKyy/TQC4TSq3adiwYRG3TZGYJ7226Y477oi4bYrEPOm5TV26dIm4bYrEPOm1Tddee23EbVMk5kmvberVq1fEbVMk5kmvberXr1/EbVMk5kmvbbr99tsjbpsiMU96bdPQoUNNvU1//PEHgiHcnGMlBTvn2LPPPovXX38dzz77LO677z6cPXsWzzzzDH7//XcUFBTg999/R9euXRU/6+vKsf79+0fEnGPp6elISEgwOgwyCPMvNuafeAyIjfkXG/MvNuZfbMy/2Myef845pqOXX34ZI0aMwBtvvIHmzZvjyiuvRFRUFEaMGAEAqFChgs/P1qxZE61atZL9adq0abhC192nn35qdAhkIOZfbMw/8RgQG/MvNuZfbMy/2Jh/sUVa/jk4FoSYmBh88v/t3Xt8z/X///H727ADNsbGnFJziI0QU5JDfT4RrXyKpRT6KCnxaZ/El/LxiVIa6WSosE6U5BD1cagccsioKZTmkNpmOc1pjWF7/f7w2ztvO9hz5n3a7Xq5vC7Z8/V8vd6Pl9fum/ej1/v1eucd7d+/X2vXrtUvv/yi5cuX6/jx4ypXrpxXNbtMRUVFuboEuBDnv2zj/IPvgbKN81+2cf7LNs5/2cb5L9u87fxzQ/4SqFmzpmrWrClJysnJ0erVq9WuXbsirxwrSN5N/r3hxvw7d+5UjRo1XF0GXITzX7Zx/sH3QNnG+S/bOP9lG+e/bOP8l23ufv7z+ix5fZdLoTlWiPT0dB0/flzh4eGqUKFCofMmTZqk9PR0vfHGG8avkZKSIkn2G/MDAAAAAACgdKSkpKh169aXnFcmm2Nvvvmmjh07pv3790s6/wjS1NRUSdLQoUMVFBSkUaNG6d1339Wvv/6qBg0aSJI++OADffrpp+rYsaMqV66sL7/8UvPmzdPDDz+se+65x7iOTp06adGiRapXr558fX1L7ficLe/BAosWLSrTHy0tqzj/ZRvnH3wPlG2c/7KN81+2cf7LNs5/2eYJ5z87O1spKSnq1KlTseaXyebYpEmT9Ntvv9m/XrBggRYsWCBJeuCBBxQUFFTgdo0bN1ZGRobGjx+vU6dOqUmTJpo+fboGDRpUojqqVq2qu+66q0TbuqOGDRt6/FM3UXKc/7KN8w++B8o2zn/Zxvkv2zj/ZRvnv2xz9/NfnCvG8pTJ5ti+ffsuOSchIUEJCQkOY1FRUVqzZs2VKQoAAAAAAABOx9MqAQAAAAAAUGbRHAMAAAAAAECZRXMMly0kJERjx45VSEiIq0uBC3D+yzbOP/geKNs4/2Ub579s4/yXbZz/ss0bz7/NsizL1UUAAAAAAAAArsCVYwAAAAAAACizaI4BAAAAAACgzKI5BgAAAAAAgDKL5hgAAAAAAADKLJpjAAAAAAAAKLNojgEAAAAAAKDMojmGEsvOztbIkSNVu3Zt+fv7q127dlq5cqWry0Ixbd68WU888YQiIiJUqVIl1a9fXzExMUpOTnaYN2DAANlstnzLtddem2+fubm5evnll3X11VfLz89PLVq00Ny5cwt8/Z9//lndunVT5cqVFRwcrAcffFCHDh26IseK/FavXl3gebXZbPr2228d5m7YsEEdOnRQQECAatWqpWHDhikzMzPfPk1+JhR3n7gyCst13pKWliZJ6ty5c4Hru3Xrlm+fnH/3lJmZqbFjx6pbt24KDg6WzWZTQkJCgXOL+3P5SvysN9kniq845z83N1cJCQm68847Va9ePVWqVEmRkZF6/vnndfr06Xz7LOznxksvvZRvblpammJiYlS1alUFBgbqrrvu0t69ewusdebMmWratKn8/PzUqFEjvfHGG6Xyd1CWFTf/rv63Hvm/Mop7/ov698Df//53+7x9+/YVOu+jjz7Kt1/Ov2sV972exO//POVd8qrwCgMGDND8+fP15JNPqlGjRkpISFD37t21atUqdejQwdXl4RImTpyo9evXq3fv3mrRooX++OMPvfnmm2rdurW+/fZbRUZG2uf6+vrqnXfecdg+KCgo3z6feeYZvfTSS3rkkUfUtm1bLV68WPfff79sNpv69Oljn5eamqqOHTsqKChIEyZMUGZmpiZNmqRt27YpMTFRFStWvHIHDgfDhg1T27ZtHcYaNmxo//PWrVt16623qmnTpnrllVeUmpqqSZMmadeuXfrf//7nsF1xfyaY7BNXxqOPPqq//e1vDmOWZWnw4MFq0KCB6tSpYx+vW7euXnzxRYe5tWvXzrdPzr97Onz4sMaNG6f69evruuuu0+rVqwucZ/Jz+Ur8rC/uPmGmOOc/KytLDz30kG644QYNHjxYoaGh2rhxo8aOHauvvvpKX3/9tWw2m8M2f//739WvXz+HsVatWjl8nZmZqS5duuj48eMaPXq0KlSooClTpqhTp07aunWrqlevbp87Y8YMDR48WPfcc4/+/e9/65tvvtGwYcOUlZWlkSNHlt5fSBlT3PxLrv23Hvm/Mop7/t9///18Y1u2bNFrr72m2267Ld+6++67T927d3cYu/HGGx2+5vy7XnHf6/H7/wIWUAKbNm2yJFlxcXH2sVOnTlnh4eHWjTfe6MLKUFzr16+3srOzHcaSk5MtX19fq2/fvvax/v37W5UqVbrk/lJTU60KFSpYQ4YMsY/l5uZaN998s1W3bl3r3Llz9vHHHnvM8vf3t3777Tf72MqVKy1J1owZMy7nsFBMq1atsiRZn3zySZHzbr/9dissLMw6fvy4feztt9+2JFnLly+3j5n8TCjuPuFc33zzjSXJeuGFF+xjnTp1siIiIi65LefffZ0+fdpKT0+3LMuyNm/ebEmyZs+enW9ecX8uX4mf9Sb7hJninP/s7Gxr/fr1+bZ97rnnLEnWypUrHcYlOZyrwkycONGSZCUmJtrHfv75Z8vHx8caNWqUfSwrK8uqXr261aNHD4ft+/bta1WqVMnKyMi45GuhYMXNvyv/rUf+r5zinv+CDBw40LLZbFZKSop97Ndff833u74wnH/XK+57PX7//4XmGErk6aeftnx8fBze3FiWZU2YMMGSZP3+++8uqgyXq3Xr1lbr1q3tX+f9g+ncuXP5zveFpk6dakmyduzY4TA+Z84cS5L1zTff2MdCQ0Ot3r1759tH48aNrVtvvbUUjgKXcmFz7MSJE9bZs2fzzTl+/LhVvnx56+mnn3YYz87OtipXrmwNHDjQPlbcnwkm+4RzPfbYY5bNZrN+/fVX+1hec+zs2bPWyZMnC92W8+8ZinpzVNyfy1fiZ73JPlFypm+Of/zxR0uS9frrrzuM5zXHsrKyrFOnThW6fdu2ba22bdvmG7/tttus8PBw+9eff/65Jcn6/PPPHeZt2LDBkmS9//77xaoXRStOc8wV/9Yj/85hkv/Tp09bVatWtTp37uwwfmFzLDMzM1/j5UKcf/d18Xs9fv//hXuOoUSSkpLUuHFjBQYGOoxHRUVJOv+xGXgey7J04MAB1ahRw2E8KytLgYGBCgoKUnBwsIYMGZLv/kBJSUmqVKmSmjZt6jCe9z2RlJQk6fz9Rw4ePKg2bdrke/2oqCj7PDjHQw89pMDAQPn5+alLly7asmWLfd22bdt07ty5fOeqYsWKatmypcO5Ku7PBJN9wnnOnj2refPmqX379mrQoIHDuuTkZFWqVElVqlRRrVq1NGbMGJ09e9ZhDuffs5n8XL4SP+uLu0841x9//CFJ+f5NIEkJCQmqVKmS/P391axZM82ZM8dhfW5urn788cdCz/+ePXt08uRJSX+d34vnXn/99SpXrhzn30lc9W898u9+vvjiCx07dkx9+/YtcP1zzz2nypUry8/PT23bttWKFSsc1nP+3dfF7/X4/e+Ie46hRNLT0xUWFpZvPG9s//79zi4JpeDDDz9UWlqaxo0bZx8LCwvTiBEj1Lp1a+Xm5mrZsmWKj4/XDz/8oNWrV6t8+fM/RtLT01WzZs189yW5+HsiPT3dYfziuRkZGcrOzpavr+8VOUacV7FiRd1zzz3q3r27atSooZ9++kmTJk3SzTffrA0bNqhVq1aXPFfffPON/evi/kww2SecZ/ny5Tpy5Ei+fwiHh4erS5cuat68uf7880/Nnz9fzz//vJKTk/Xxxx/b53H+PZvJz+Ur8bO+uPuEc7388ssKDAzU7bff7jDevn17xcTE6Oqrr9b+/fs1depU9e3bV8ePH9djjz0mSfbze6mfC02aNFF6erp8fHwUGhrqMK9ixYqqXr06598JXPlvPfLvfj788EP5+vqqV69eDuPlypXTbbfdpn/84x+qU6eO9u7dq1deeUW33367PvvsM/Xo0UMS59+dXfxej9//jmiOoUROnTpVYPPCz8/Pvh6eZefOnRoyZIhuvPFG9e/f3z5+8Y24+/Tpo8aNG+uZZ57R/Pnz7TdKLO73RN5/LzWX5tiV1b59e7Vv397+9Z133qlevXqpRYsWGjVqlJYtW3bJc3Vhzkvr/POzwzXmzJmjChUqKCYmxmF85syZDl8/+OCDGjRokN5++23FxsbqhhtukMT593QmP5evxM96/k3hfiZMmKAvv/xS8fHxqlq1qsO69evXO3z9z3/+U9dff71Gjx6tAQMGyN/fv9jnP++/hT2Ih58LzuHKf+uRf/dy4sQJff755+revXu+7NevX1/Lly93GHvwwQfVrFkzPfXUU/bmGOffPRX0Xo/f/474WCVKxN/fX9nZ2fnG8x757e/v7+yScBn++OMP9ejRQ0FBQZo/f758fHyKnB8bG6ty5crpyy+/tI8V93si7798/7ifhg0b6q677tKqVauUk5NzyXN14XkqrfPPuXe+zMxMLV68WF27dnV4elxhnnrqKUm6Ivnn/LuGyc/lK/Gznn9TuJePP/5Yzz77rAYOHGi/EqwoFStW1BNPPKFjx47pu+++k2R+/s+cOVPgvvm54DrO+rce+Xcvn376qU6fPl3oRyovFhwcrIceeki//PKLUlNTJXH+3VFh7/X4/e+I5hhKJCwszH7J5IXyxmrXru3sklBCx48f1+23365jx45p2bJlxTp3/v7+ql69ujIyMuxjYWFh+uOPP2RZlsPci78n8i6TLez7Jzg4mKvGXKhevXo6c+aM/vzzz0ueqwu/V4r7M8Fkn3CORYsWKSsrq9j/EK5Xr54k5cs/599zmfxcvhI/64u7T1x5K1euVL9+/dSjRw9Nnz692Ntd/HMh7/wW9+dCTk6ODh486DDvzJkzOnLkCOffRZz1bz3y714+/PBDBQUF6Y477ij2Nhfnn/PvXop6r8fvf0c0x1AiLVu2VHJysk6cOOEwvmnTJvt6uL/Tp08rOjpaycnJWrp0qZo1a1as7U6ePKnDhw8rJCTEPtayZUtlZWXp559/dph78fdEnTp1FBIS4nDj9zyJiYl877jY3r175efnp8qVKysyMlLly5fPd67OnDmjrVu3Opyr4v5MMNknnOPDDz9U5cqVdeeddxZr/t69eyUpX/45/57L5OfylfhZX9x94sratGmT/vGPf6hNmzaaN2+e/T5TxXHxz4Vy5cqpefPmBZ7/TZs26ZprrlGVKlUk/XV+L567ZcsW5ebmcv5dxFn/1iP/7iM9PV2rVq3SPffcY/Q/qi/OP+fffVzqvR6//x1xzzEXOnbsmNasWaN69ep53JUyrVu3Vk5OjsaNG6eHHnpI0vk3N9OnT1fz5s114sQJ7dixw8VVoig5OTn617/+pQ0bNuiNN95QYGBgvnOWnZ2tc+fOqVKlSg7jkydPlmVZatasmX2ba6+9VuXLl9f48eP17LPPSjr/RJTJkycrNDRUQUFB9rldunTR4sWL9eWXX9r/78K3336r5ORk3XvvvXzvOEFGRoaCg4Mdxnbu3KnFixerQ4cO9l9S7dq10+zZs9WrVy/798Gnn36qzMxMRUVF2c+Vyc+E4u4TV15GRoZWrlyp7t2769dff3VYl5mZqYoVKzrcC8iyLD399NOSzn8Ml/PvWfbs2SPp/JOkLv57Lu7P5Svxs95knyi5os7/nj171K9fP9WqVUtxcXH2N7sXK+h3x59//qmXX35ZVatWlb+/v33fN998s6ZMmaKPP/5YkZGRkqRff/1VX331lQYMGGCfV6tWLQUGBurll1/WVVddZd/viy++KD8/P4WHh3P+S0Fh59/V/9Yj/85RVP7zvPfee8rNzdVNN91U4JyC8n/gwAG9/fbbaty4sTIyMuxXj3H+Xa847/Uk7/79n52drZSUFHXq1CnfPfQKZLmx06dPWyNGjLDCwsIsPz8/KyoqylqxYkWxtk1NTbV69+5tBQUFWVWqVLHuvPNOa8+ePfnmxcfHW7169bLq1atnSbL69+9f6D6PHj1qPfLII1aNGjWsgIAAq3PnztZ3331X0sOzFi1aZEliYWFhYWFhYWFhYWFhYWFhYSnlZdGiRcXqz7j1lWMDBgzQ/Pnz9eSTT6pRo0ZKSEhQ9+7dtWrVKnXo0KHQ7TIzM9WlSxcdP35co0ePVoUKFTRlyhR16tRJW7dudbjh8MSJE3Xy5ElFRUUV+LnYPLm5uerRo4d++OEHPf3006pRo4bi4+PVuXNnfffdd2rUqJHx8eV9PnvRokVq2LCh8fbuZNu2bWrevLmrywA8BpkBzJAZwAyZAcyQGcCMu2dm9+7d6tmzp73vcilu2xxLTEzURx99pLi4OA0fPlyS1K9fP0VGRmrEiBHasGFDodvGx8dr165dSkxMVNu2bSVJt99+uyIjIzV58mRNmDDBPnfNmjWqX7++bDabKleuXOg+58+frw0bNuiTTz5Rr169JEkxMTFq3Lixxo4dqzlz5hgfY95HKRs2bKiIiAjj7d1JcnKyxx8D4ExkBjBDZgAzZAYwQ2YAM56SmeLewsptb8if94jRQYMG2cf8/Pw0cOBAbdy4USkpKUVu27ZtW3tjTDr/edZbb71V8+bNc5h71VVXyWazFauemjVr6u6777aPhYSEKCYmRosXLy7wEaRlxa4DJ/Vd6p/alnrc1aUAHuPo0aOuLgHwKGQGMENmADNkBjDjbZlx2+ZYUlKSGjdurMDAQIfxqKgoSdLWrVsL3C43N1c//vij2rRpk29dVFSU9uzZo5MnT5aontatW6tcOce/sqioKGVlZSk5ObnI7Q8ePKgdO3Y4LLt37zauwx39893N+iCtmmauK/jmrQDy69ixo6tLADwKmQHMkBnADJkBzHhbZty2OZaenm5/ssGF8sb2799f4HYZGRnKzs4u0bZXop488fHxioyMdFh69uwpSVq3bp3WrFmjuLg4ZWRkqH///pKk6OhoSVJsbKx2796tWbNmaeHChUpMTNT48eOVlZWlmJgYh7mjR4/Wtm3bNGfOHM2ZM0fbtm3T6NGjHebExMQoKytL48ePV2JiohYuXKhZs2Zp9+7dio2NdZjbv39/ZWRkKC4uTmvWrNGyZcs0depUpaWlafDgwZKkI6m/SpK+2fyD0tLSNHXqVC1btsyjjylv7uDBgzkmjumKHNOoUaO87pi88TxxTO5zTBfvzxuOyRvPE8fkPsfUvXt3rzsmbzxPHJP7HNPQoUO97pi88TxxTO5zTK+++qpbH9PmzZtlwmZZlmW0hZOEh4erSZMm+uKLLxzG9+7dq/DwcE2ZMkVPPvlkvu1SUlJUv359TZw4USNGjHBYN2vWLA0cOFBJSUlq2bJlvm0rV66sXr16KSEhId86Hx8fPfroo4qPj3cY//rrr3Xrrbdq4cKF9mZXQQ4ePKhDhw45jOXdIG779u0e8Vndwjz6/hYt33FAjWtW1orYTq4uBwAAAAAAlGE7duxQZGRksfstbnvlmL+/f4H38Tp9+rR9fWHbSSrRtleinjyhoaGKiIhwWDz9CZV5alQ+f4O7QyfL7n3XAFN5/4cDQPGQGcAMmQHMkBnAjLdlxm2bY2FhYUpPT883njdWu3btArcLDg6Wr69viba9EvWUBSFVzjfHjmad1ZlzuS6uBvAMS5YscXUJgEchM4AZMgOYITOAGW/LjNs2x1q2bKnk5GSdOHHCYXzTpk329QUpV66cmjdvri1btuRbt2nTJl1zzTWqUqVKier5/vvvlZvr2PzZtGmTAgIC1LhxY+N9eou85pgkHfmTq8eA4sj7LD+A4iEzgBkyA5ghM4AZb8uM2zbHevXqpZycHL311lv2sezsbM2ePVvt2rVTvXr1JEm///67du7cmW/bzZs3OzTIfvnlF3399dfq3bt3ies5cOCAFixYYB87fPiwPvnkE0VHR8vX17eIrb1bSOW/jp2PVgLFM2XKFFeXAHgUMgOYITOAGTIDmPG2zLhtc6xdu3bq3bu3Ro0apREjRuitt97SLbfcon379unll1+2z+vXr5+aNm3qsO3jjz+u8PBw9ejRQ3FxcXr11Vf197//XTVr1tRTTz3lMHfJkiV6/vnn9fzzz+vs2bP68ccf7V//+OOP9nm9evXSDTfcoIceekjjxo1TfHy8OnfurJycHD333HNX9i/DzV145RjNMaB4Zs6c6eoSAI9CZgAzZAYwQ2YAM96WmfKuLqAo7733nsaMGaP3339fR48eVYsWLbR06VJ17NixyO2qVKmi1atXKzY2Vs8//7xyc3PVuXNnTZkyRSEhIQ5zP/30U7377rv2r5OSkpSUlCRJqlu3rlq0aCHp/NMqv/jiCz399NN6/fXXderUKbVt21YJCQlq0qRJKR+5Z6E5BpiLiopydQmARyEzgBkyA5ghM4AZb8uMWzfH/Pz8FBcXp7i4uELnrF69usDxunXr6pNPPrnkayQkJCghIaFY9VSrVk3vvPOO3nnnnWLNLytq8LFKwNipU6dcXQLgUcgMYIbMAGbIDGDG2zLjth+rhOfwq+AjX1uOJOlQJs0xoDj27Nnj6hIAj0JmADNkBjBDZgAz3pYZmmMoFbWqBkjiyjGguHr27OnqEgCPQmYAM2QGMENmADPelhmaYygVJw+mSaI5BhTX+PHjXV0C4FHIDGCGzABmyAxgxtsyY7Msy3J1EWXVjh07FBkZqe3btysiIsLV5VyWoXOTtOSH/WpQPUCrn+7i6nIAAAAAAEAZZdpv4coxlIoNXy+TxJVjQHFFR0e7ugTAo5AZwAyZAcyQGcCMt2WG5hhKxcN9YyRJf57J0Z/Z51xcDeD+lixZ4uoSAI9CZgAzZAYwQ2YAM96WGZpjKBVffDrX/ufDPLESuKTBgwe7ugTAo5AZwAyZAcyQGcCMt2WG5hhKxUP33WP/Mx+tBC5tzJgxri4B8ChkBjBDZgAzZAYw422ZoTmGUvHDpm/sf6Y5BlzaokWLXF0C4FHIDGCGzABmyAxgxtsyQ3MMpSIivJ79z4f4WCVwSeHh4a4uAfAoZAYwQ2YAM2QGMONtmaE5hlIREuivcrbzf+bKMeDS/P39XV0C4FHIDGCGzABmyAxgxtsyQ3MMpWLL5s0KruQrieYYUByJiYmuLgHwKGQGMENmADNkBjDjbZmhOYZSMXDgQIVUoTkGFNfAgQNdXQLgUcgMYIbMAGbIDGDG2zJDcwylIjY29q/mGPccAy4pNjbW1SUAHoXMAGbIDGCGzABmvC0zNsuyLFcXUVbt2LFDkZGR2r59uyIiIlxdzmV7at4P+vT7VIUF+WnjqFtdXQ4AAAAAACiDTPstXDmGUhEdHW2/cuxwZrZyc+m5AkWJjo52dQmARyEzgBkyA5ghM4AZb8sMzTGUiiVLltibY2dzLB0/ddbFFQHubcmSJa4uAfAoZAYwQ2YAM2QGMONtmaE5hlJx4T3HJO47BlyKt31GH7jSyAxghswAZsgMYMbbMkNzDKViyJAhCql8QXOMJ1YCRRoyZIirSwA8CpkBzJAZwAyZAcx4W2Yuuzk2btw4bd++vdD1O3bs0Lhx4y73ZeDm1q5d63jlGM0xoEhr1651dQmARyEzgBkyA5ghM4AZb8vMZTfH/vvf/+rHH38sdP327dv13HPPXe7LwM1Vq1bNoTl2mI9VAkWqVq2aq0sAPAqZAcyQGcAMmQHMeFtmrvjHKjMyMlSxYsUr/TJwsTp16ijQr7wqlj//LcWVY0DR6tSp4+oSAI9CZgAzZAYwQ2YAM96WmfIl2Wjt2rVavXq1/esFCxZo9+7d+eYdO3ZMH3/8sZo3b17iAuEZli9frqioKIVU9lXasVM0x4BLyMsMgOIhM4AZMgOYITOAGW/LjM2yLMt0o+eee87+UUmbzaaidtGsWTPNnDlT7dq1K3mVXmrHjh2KjIzU9u3bFRER4epyLktWVpYCAgLUc+p6bU05ppsb1dD7AznnQGHyMgOgeMgMYIbMAGbIDGDG3TNj2m8p0ccqR4wYoUOHDungwYOyLEvTp0/XoUOHHJbDhw8rKytL27dvpzFWBgwYMECS7Pcd48oxoGh5mQFQPGQGMENmADNkBjDjbZkp0ccq/f395e/vL0n69ddfFRIS4tYdQ1x58+bNk0RzDCiuvMwAKB4yA5ghM4AZMgOY8bbMXPYN+a+66ioaY1B0dLQkKaTy+eZYRtYZnc3JdWVJgFvLywyA4iEzgBkyA5ghM4AZb8vMZTfHLMvSjBkzFBUVpRo1asjHxyffUr58iS5QgwdZsmSJJKnG/79yzLKkjD/PuLIkwK3lZQZA8ZAZwAyZAcyQGcCMt2XmsptjI0aM0OOPP66cnBw98MAD+s9//pNvGTNmTGnUCjc2evRoSX9dOSbx0UqgKHmZAVA8ZAYwQ2YAM2QGMONtmbnsS7reffdd3XPPPV73eVOYue+++yT9dc8xieYYUJS8zAAoHjIDmCEzgBkyA5jxtsxc9pVjp06d0t/+9rfSqAUebNu2bZKkUJpjQLHkZQZA8ZAZwAyZAcyQGcCMt2Xmsptjt956qzZv3lwatcAL1LjwY5WZNMcAAAAAAIB7u+zmWHx8vL799ltNmDBBR44cKY2a4IGaN28uSfKv6KMqvuc/rcuVY0Dh8jIDoHjIDGCGzABmyAxgxtsyY9wcq1KligIDA+3Ltddeqz179mjMmDEKDQ1VpUqVHNYHBgYqKCjoStQONzJ37lz7n/PuO0ZzDCjchZkBcGlkBjBDZgAzZAYw422ZsVmWZZlsMGDAANlsNuMXmj17tvE23m7Hjh2KjIzU9u3bFRER4epySk3MjI1K/DVDUQ2CNW/wja4uBwAAAAAAlCGm/Rbjp1UmJCSUpC54uejoaC1ZskTSBVeOcc8xoFAXZgbApZEZwAyZAcyQGcCMt2Xmsu85BkhyCEVIZT5WCVyKN/0iAZyBzABmyAxghswAZrwtM8ZXjl3svffeK3K9zWaTn5+f6tatq9atW8vX17fI+fBMMTExmjdvnqS/rhzLzD6nU2dy5F/Rx5WlAW7pwswAuDQyA5ghM4AZMgOY8bbMGN9z7GLlypWz34Ps4l1dOG6z2RQYGKhRo0ZpxIgRl/OSXsOb7jmWlZWlgIAASdK8LSkaMf9HSdI3I7qoXnCAK0sD3NKFmQFwaWQGMENmADNkBjDj7pkx7bdc9scqt27dqhYtWqhLly769NNP9cMPP+iHH37Q/Pnz1blzZ7Vs2VLr16/Xp59+qtatW2vUqFGaNm3a5b4s3MzkyZPtf867ckySDvLRSqBAF2YGwKWRGcAMmQHMkBnAjLdl5rKbY1OmTFHNmjX15Zdf6h//+IeaN2+u5s2b6+6779aXX36pkJAQzZw5Uz179tTKlSt1ww03KD4+vjRqhxvp2rWr/c959xyTuO8YUJgLMwPg0sgMYIbMAGbIDGDG2zJz2c2xRYsW6a677ipwnc1m05133qkFCxacf7Fy5XTPPfdo9+7dxdp3dna2Ro4cqdq1a8vf31/t2rXTypUri7VtWlqaYmJiVLVqVQUGBuquu+7S3r17C5w7c+ZMNW3aVH5+fmrUqJHeeOONfHP++9//ymaz5Vv8/PyKVY+3S0tLs/859IIrx3hiJVCwCzMD4NLIDGCGzABmyAxgxtsyc9k35M/NzdUvv/xS6PqdO3cqNzfX/rWvr2+xG0oDBgzQ/Pnz9eSTT6pRo0ZKSEhQ9+7dtWrVKnXo0KHQ7TIzM9WlSxcdP35co0ePVoUKFTRlyhR16tRJW7duVfXq1e1zZ8yYocGDB+uee+7Rv//9b33zzTcaNmyYsrKyNHLkyHz7njZtmipXrmz/2seHm81L0tGjR+1/Dq5UUTabZFlcOQYU5sLMALg0MgOYITOAGTIDmPG2zFx2c+zOO+9UfHy8GjZsqIcfftje+Dp9+rTefvttTZ8+Xffee699/saNG9WwYcNL7jcxMVEfffSR4uLiNHz4cElSv379FBkZqREjRmjDhg2FbhsfH69du3YpMTFRbdu2lSTdfvvtioyM1OTJkzVhwgRJ0qlTp/TMM8+oR48emj9/viTpkUceUW5ursaPH69BgwapWrVqDvvu1auXatSoYfA3VDZ07NjR/ufyPuVUvVJFHc48Q3MMKMSFmQFwaWQGMENmADNkBjDjbZm57I9Vvvbaa2rTpo2GDRumqlWr6uqrr9bVV1+tqlWr6l//+pdat26t1157TdL5hpm/v7/+/e9/X3K/8+fPl4+PjwYNGmQf8/Pz08CBA7Vx40alpKQUuW3btm3tjTFJuvbaa3Xrrbc6PGp01apVOnLkiB5//HGH7YcMGaI///xTn3/+eb59W5alEydO5HsyZ1k3depUh69r/P/7jtEcAwp2cWYAFI3MAGbIDGCGzABmvC0zl33lWHBwsNavX6+FCxdq+fLl+u233yRJt912m7p27aqePXuqXLnzPTg/Pz+9/fbbxdpvUlKSGjdurMDAQIfxqKgoSeefklmvXr182+Xm5urHH3/UP//5z3zroqKitGLFCp08eVJVqlRRUlKSJKlNmzYO866//nqVK1dOSUlJeuCBBxzWXXPNNcrMzFSlSpXUs2dPTZ48WTVr1rzk8Rw8eFCHDh1yGCvuvdc8wZQpUxy+Dqniq51/nOSeY0AhLs4MgKKRGcAMmQHMkBnAjLdl5rKvHJPO33j/7rvv1owZM7Rs2TItW7ZMM2bM0N13321vjJlKT09XWFhYvvG8sf379xe4XUZGhrKzs4u1bXp6unx8fBQaGuowr2LFiqpevbrDa1SrVk1PPPGEZsyYofnz5+vhhx/Wxx9/rJtvvlknTpy45PHEx8crMjLSYenZs6ckad26dVqzZo3i4uKUkZGh/v37S5Kio6MlSbGxsdq9e7dmzZqlhQsXKjExUePHj1dWVpZiYmIc5o4ePVrbtm3TnDlzNGfOHG3btk2jR492mBMTE6OsrCyNHz9eiYmJWrhwoWbNmqXdu3crNjbWYW7//v2VkZGhuLg4rVmzRsuWLdPUqVOVlpamwYMH2+dGR0dr8ODBSktL09SpU3XmxBFJ0p60gx57TJIcjmnZsmUef544Jvc5pvbt23vdMXnjeeKY3OeYrrnmGq87Jm88TxyT+xxT7dq1ve6YvPE8cUzuc0ytW7f2umPyxvPEMbnPMXXv3t2tj2nz5s0yYbPc9POB4eHhatKkib744guH8b179yo8PFxTpkzRk08+mW+7lJQU1a9fXxMnTtSIESMc1s2aNUsDBw5UUlKSWrZsqYEDB2ru3LnKysrKt5/69eurdevWWrRoUaE1zpkzR3379tWLL76o//u//yvyeAq7cqxnz57avn27IiIiitze07z4xc+asXavKvqU0y/Pd5PNZnN1SQAAAAAAoAzYsWOHIiMji91vMb6s6+qrr1Z4eLjOnj1r//qaa64pcgkPDzc+EH9/f2Vn5/9I3unTp+3rC9tOUrG29ff315kzZwrcT9790Ypy//33q1atWvryyy+LnCdJoaGhioiIcFiK82ACT5HXXc4TUuX8PcfO5OTqxKlzrigJcGsXZwZA0cgMYIbMAGbIDGDG2zJjfM+xTp06yWaz2T8umfd1aQsLC1NaWlq+8fT0dEnnLxUvSHBwsHx9fe3zito2LCxMOTk5OnjwoMNHK8+cOaMjR44U+hoXqlevnjIyMi59QF6uoHuO5TmUeVpBARWcXRLg1rztM/rAlUZmADNkBjBDZgAz3pYZ4+ZYQkJCkV+XlpYtW2rVqlU6ceKEw035N23aZF9fkHLlyql58+basmVLvnWbNm3SNddcoypVqjjsY8uWLerevbt93pYtW5Sbm1voa+SxLEv79u1Tq1atDI7MO82cOVNPP/20/euQyn81xw6ezFbD0CquKAtwWxdnBkDRyAxghswAZsgMYMbbMlMqN+S/Enr16qWcnBy99dZb9rHs7GzNnj1b7dq1sz+p8vfff9fOnTvzbbt582aHBtkvv/yir7/+Wr1797aP3XLLLQoODta0adMctp82bZoCAgLUo0cP+9jF9wvLm3fo0CF169bt8g7WC+Q9RTSPw5VjJ3liJXCxizMDoGhkBjBDZgAzZAYw422ZMb5yrCAnTpxQfHy8Vq1apYMHD2rGjBmKiopSRkaGEhISdOeddxrfX6tdu3bq3bu3Ro0apYMHD6phw4Z69913tW/fPs2cOdM+r1+/flqzZo0ufK7A448/rrfffls9evTQ8OHDVaFCBb3yyiuqWbOmnnrqKfs8f39/jR8/XkOGDFHv3r3VtWtXffPNN/rggw/0wgsvKDg42D73qquu0r333qvmzZvLz89P69at00cffaSWLVvq0UcfvYy/Pe9w6tQph69pjgFFuzgzAIpGZgAzZAYwQ2YAM96WmctujqWmpqpTp05KSUlRo0aNtHPnTmVmZko6f/+vGTNm6LffftNrr71mvO/33ntPY8aM0fvvv6+jR4+qRYsWWrp0qTp27FjkdlWqVNHq1asVGxur559/Xrm5uercubOmTJmikJAQh7mPP/64KlSooMmTJ+uzzz5TvXr1NGXKFP3rX/9ymNe3b19t2LBBn376qU6fPq2rrrpKI0aM0DPPPKOAgADjY/M2e/bscfg6yL+CKvjYdDbH0qFMmmPAxS7ODICikRnADJkBzJAZwIy3ZcZmXXjJVQncd999+uqrr7R69WqFhoYqNDRUX375pW655RZJ0siRI7V06VLt2LGjVAr2JqaPFnVnaWlpqlOnjsNY+xe/0v7jp3V36zp6JaalawoD3FRBmQFQODIDmCEzgBkyA5hx98yY9lsu+55jK1as0LBhw9SsWbMCn1p5zTXXKCUl5XJfBm5u/Pjx+cbyPlrJxyqB/ArKDIDCkRnADJkBzJAZwIy3Zeaym2OnTp3K91HFC508efJyXwIeYPr06fnG8ppjhzPPOLscwO0VlBkAhSMzgBkyA5ghM4AZb8vMZTfHmjVrprVr1xa6ftGiRWrVqtXlvgzcXHR0dL4xrhwDCldQZgAUjswAZsgMYIbMAGa8LTOX3Rx78skn9dFHH2nixIk6fvy4JCk3N1e7d+/Wgw8+qI0bNyo2NvayC4V7W7JkSb6xkMrnm2MZf2YrJ/eybm0HeJ2CMgOgcGQGMENmADNkBjDjbZm57ObYAw88oHHjxunZZ59V48aNJUndunVTkyZN9NFHH2nChAnq2bPn5b4M3NzgwYPzjeVdOZZrSUf+5Oox4EIFZQZA4cgMYIbMAGbIDGDG2zJToqdVdujQQTfffLNuuukm3XTTTapWrZp+//13LViwQLt27VJubq7Cw8N1991365prrrkSdXsFb39a5bLt6Rr8wfeSpM+HdVBE7SBXlAa4JXd/ugvgbsgMYIbMAGbIDGDG3TPjlKdV/v7775o4caLuuusuhYSEKDIyUi+++KJq1Kihp59+WtOmTdPw4cNpjJUhixYtyjdW4/9/rFLivmPAxQrKDIDCkRnADJkBzJAZwIy3ZaZ8STb6/ffflZqaqnXr1mndunXasGGD3nnnHc2YMUM2m021a9fWTTfdpA4dOqhDhw667rrrZLPZSrt2uJHw8PB8Y3kfq5RojgEXKygzAApHZgAzZAYwQ2YAM96WmRI1xySpbt266tOnj/r06SNJyszM1IYNG7R+/XqtW7dOn3/+uT755BNJUmBgoI4ePVo6FcMt+fv75xtzuHIsk+YYcKGCMgOgcGQGMENmADNkBjDjbZm57Bvy56lcubJuu+02Pffcc/rggw80Y8YM3XDDDbIsSydOnCitl4GbSkxMzDdWybe8KlX0kcSVY8DFCsoMgMKRGcAMmQHMkBnAjLdlpsRXjl1o+/btWrdundavX6/169frt99+k6+vr1q1aqWnnnpKN910U2m8DNzYwIEDCxwPqeKrP49k0RwDLlJYZgAUjMwAZsgMYIbMAGa8LTMlunJszZo1mjBhgrp3767g4GC1aNFCzz33nLKysjRkyBCtW7dOx44d0/r16xUXF6eePXuWctlwN7GxsQWO5913jOYY4KiwzAAoGJkBzJAZwAyZAcx4W2ZslmVZphuVK1dOFSpUUO/evXX77bfrxhtv5MmUJWD6aFFP9PiH3+mLbX/ompBK+vqpzq4uBwAAAAAAeDnTfkuJrhxr3ry5cnJyNHfuXL388suKi4vThx9+qF9//bUku4MXiI6OLnA8pDJXjgEFKSwzAApGZgAzZAYwQ2YAM96WmRJdOSZJJ0+e1MaNG+33Gdu0aZOysrIUGhqq9u3b66abblL79u11/fXXq0KFCqVdt1coC1eOvfn1Lk1akSxJ2jm+m/wq+Li4IgAAAAAA4M2ccuWYJFWpUsX+dMovv/xSx44dU2JiokaNGqUKFSpoypQpuummmxQUFKSOHTuW9GXgIS51zzGJq8eAC3nbZ/SBK43MAGbIDGCGzABmvC0zpfK0Skny8fHR9ddfr+uvv15dunTRN998ow8//NB+dRm825AhQwocd2iOZWarXnCAs0oC3FphmQFQMDIDmCEzgBkyA5jxtsxcdnMsOztbmzZt0rp167Ru3Tp9++23On78uCTJ19dXN998szp06HDZhcK9rV27Vg0bNsw3HlLZz/5nrhwD/lJYZgAUjMwAZsgMYIbMAGa8LTMlao4tXrzY3gxLSkrS2bNnZVmWqlevbm+GdejQQW3atOF+Y2VEtWrVChy/8Mqxw5k0x4A8hWUGQMHIDGCGzABmyAxgxtsyU6Lm2D/+8Q9J0tVXX617773X3gxr2rRpqRYHz1GnTp0Cx6tXrmj/M1eOAX8pLDMACkZmADNkBjBDZgAz3paZEt2Q/+OPP1ZaWpr27Nmjd999V4888giNsTJu+fLlBY5X8Cmn4ErnG2Q0x4C/FJYZAAUjM4AZMgOYITOAGW/LjM2yLMvVRZRVpo8WdWdZWVkKCCj4Zvtdp6zVLwdO6rZmNfVWvzZOrgxwT0VlBkB+ZAYwQ2YAM2QGMOPumTHtt5ToyjHgYgMGDCh0Xd59xw5xzzHArqjMAMiPzABmyAxghswAZrwtMzTHUCrmzZtX6LoalflYJXCxojIDID8yA5ghM4AZMgOY8bbM0BxDqYiOji50nf3KsZPZ4lO8wHlFZQZAfmQGMENmADNkBjDjbZmhOYZSsWTJkkLX5TXHss/l6mT2OWeVBLi1ojIDID8yA5ghM4AZMgOY8bbM0BxDqRg9enSh6/KaYxIfrQTyFJUZAPmRGcAMmQHMkBnAjLdlhuYYSsV9991X6LqQyn72P9McA84rKjMA8iMzgBkyA5ghM4AZb8sMzTGUim3bthW6jivHgPyKygyA/MgMYIbMAGbIDGDG2zJDcwxXHM0xAAAAAADgrmiOoVQ0b9680HVV/SuofDmbJOlQJs0xQCo6MwDyIzOAGTIDmCEzgBlvywzNMZSKuXPnFrquXDmbalQ+f/UYV44B5xWVGQD5kRnADJkBzJAZwIy3ZcZmWZbl6iLKqh07digyMlLbt29XRESEq8u5oqLfWKdtacfVqXGI3v1nlKvLAQAAAAAAXsq038KVYygV0dHRRa7Pu+8YV44B510qMwAckRnADJkBzJAZwIy3ZYbmGErFkiVLilwfkvexSu45Bki6dGYAOCIzgBkyA5ghM4AZb8sMzTGUipiYmCLX5105diQzWzm5fJIXuFRmADgiM4AZMgOYITOAGW/LDM0xlIqEhIQi1+c1x3It6WjWGSdUBLi3S2UGgCMyA5ghM4AZMgOY8bbM0BxDqZg8eXKR6/OaYxL3HQOkS2cGgCMyA5ghM4AZMgOY8bbM0BxDqejatWuR62mOAY4ulRkAjsgMYIbMAGbIDGDG2zJDcwylIi0trcj1eTfkl2iOAdKlMwPAEZkBzJAZwAyZAcx4W2bcujmWnZ2tkSNHqnbt2vL391e7du20cuXKYm2blpammJgYVa1aVYGBgbrrrru0d+/eAufOnDlTTZs2lZ+fnxo1aqQ33njjsvdZ1hw9erTI9TUuvHKMJ1YCl8wMAEdkBjBDZgAzZAYw422Zcevm2IABA/TKK6+ob9++eu211+Tj46Pu3btr3bp1RW6XmZmpLl26aM2aNRo9erSee+45JSUlqVOnTjpy5IjD3BkzZujhhx9WRESE3njjDd14440aNmyYJk6cWOJ9lkUdO3Yscn2lij7yr+AjiSvHAOnSmQHgiMwAZsgMYIbMAGa8LTNu2xxLTEzURx99pBdffFFxcXEaNGiQvv76a1111VUaMWJEkdvGx8dr165dWrp0qUaMGKHY2FitWLFC6enpDjeNO3XqlJ555hn16NFD8+fP1yOPPKL33ntPffv21fjx4x06ocXdZ1k1derUItfbbDb7fcdojgGXzgwAR2QGMENmADNkBjDjbZlx2+bY/Pnz5ePjo0GDBtnH/Pz8NHDgQG3cuFEpKSlFbtu2bVu1bdvWPnbttdfq1ltv1bx58+xjq1at0pEjR/T44487bD9kyBD9+eef+vzzz433WVZNmTLlknNojgF/KU5mAPyFzABmyAxghswAZrwtM+VdXUBhkpKS1LhxYwUGBjqMR0VFSZK2bt2qevXq5dsuNzdXP/74o/75z3/mWxcVFaUVK1bo5MmTqlKlipKSkiRJbdq0cZh3/fXXq1y5ckpKStIDDzxgtM+yKjo6WkuWLClyTt5N+benHdcj721xRlmA29r07bdqd8MNri4D8BhkBjBDZgAzZAb4y8huTdQwtOj+RnF6AJ7EbZtj6enpCgsLyzeeN7Z///4Ct8vIyFB2dvYlt23SpInS09Pl4+Oj0NBQh3kVK1ZU9erV7a9hss/CHDx4UIcOHXIY2717d6HzPU1xQhFW1U+SdDL7nFb+dOBKlwS4t8CryQFggswAZsgMYIbMAHaPdrzmknO8qTEmufHHKk+dOiVfX998435+fvb1hW0nqVjbnjp1ShUrVixwP35+fg7zirvPwsTHxysyMtJh6dmzpyRp3bp1WrNmjeLi4pSRkaH+/ftLOt+JlaTY2Fjt3r1bs2bN0sKFC5WYmKjx48crKytLMTExDnNHjx6tbdu2ac6cOZozZ462bdum0aNHO8yJiYlRVlaWxo8fr8TERC1cuFCzZs3S7t27FRsb6zC3f//+ysjIUFxcnNasWaNly5Zp6tSpSktL0+DBg+1z+/fvr8GDBystLU1Tp07VsmXL8h1TvxsbqHJmipqGBSoo96TCg31V0/esagfk6qogHwWXy1KTmpUVkJ2hpmGB8jt1WE3DAlVVmbq6WgWF+eUozC9HV1eroKrKdJgTkJ2hJjUrK7hclq4K8lHtgFzV9D2r8GBfBeWedJhb6exRNQ4NUHWf06pfpZzqVpJCKpxRwxp+qpJz3GFulZzjaljDTyEVzqhuJal+lXKq7nNajUMDVOnsUYe5HBPHZHJMvlmHvO6YvPE8cUzuc0y24/u97pi88TxxTO5zTDqa6nXH5I3niWNyn2OqkHnA647JG88Tx+ScY/Kv6HPJfkTfvn3dph8hKV8/YvPmzUX2aC5msyzLMtrCSSIjI1WzZk199dVXDuM//fSTIiIiNH36dD366KP5tjt8+LBCQkI0btw4jRkzxmFdfHy8hgwZop07d6pJkyZ64oknNH36dJ07dy7ffkJDQ3Xrrbdq7ty5RvssTGFXjvXs2VPbt29XRETEJf9O3FlGRoaCg4NdXQbgMcgMYIbMAGbIDGCGzABm3D0zO3bsUGRkZLH7LW575VhYWJjS09PzjeeN1a5du8DtgoOD5evrW6xtw8LClJOTo4MHDzrMO3PmjI4cOWKfZ7LPwoSGhioiIsJhadiwYZHbeJKZM2e6ugTAo5AZwAyZAcyQGcAMmQHMeFtm3LY51rJlSyUnJ+vEiRMO45s2bbKvL0i5cuXUvHlzbdmS/4bvmzZt0jXXXGO/cX7ePi6eu2XLFuXm5trXm+yzrMp7UAKA4iEzgBkyA5ghM4AZMgOY8bbMuO0N+Xv16qVJkybprbfe0vDhwyVJ2dnZmj17ttq1a2d/UuXvv/+urKwsXXvttQ7b/t///Z+2bNlifxLlL7/8oq+//tq+L0m65ZZbFBwcrGnTpql79+728WnTpikgIEA9evQw3qeJ7OxsSd5xY/6dO3eqRo0ari4D8BhkBjBDZgAzZAYwQ2YAM+6embw+S17f5ZIsN9a7d2+rfPny1tNPP23NmDHDat++vVW+fHlrzZo19jmdOnWyLj6MEydOWOHh4VZoaKj18ssvW1OmTLHq1atn1a5d2zp48KDD3KlTp1qSrF69ellvv/221a9fP0uS9cILL5R4n8W1aNEiSxILCwsLCwsLCwsLCwsLCwsLSykvixYtKlZ/xm1vyC9Jp0+f1pgxY/TBBx/o6NGjatGihcaPH6+uXbva53Tu3Flr1qzRxYeRmpqq2NhYrVixQrm5uercubOmTJlS4H2+3n77bU2ePFm//vqr6tWrpyeeeEL/+te/ZLPZSrzP4jh27JjWrFmjevXqFfgkTE+R92CBRYsWedV91IArhcwAZsgMYIbMAGbIDGDGEzKTnZ2tlJQUderUSVWrVr3kfLdujsEzmD4FAijryAxghswAZsgMYIbMAGa8MTNue0N+AAAAAAAA4EqjOQYAAAAAAIAyi+YYAAAAAAAAyiyaY7hsISEhGjt2rEJCQlxdCuARyAxghswAZsgMYIbMAGa8MTPckB8AAAAAAABlFleOAQAAAAAAoMyiOQYAAAAAAIAyi+YYAAAAAAAAyiyaYwAAAAAAACizaI4BAAAAAACgzKI5hhLLzs7WyJEjVbt2bfn7+6tdu3ZauXKlq8sCXG7z5s164oknFBERoUqVKql+/fqKiYlRcnJyvrk///yzunXrpsqVKys4OFgPPvigDh065IKqAffywgsvyGazKTIyMt+6DRs2qEOHDgoICFCtWrU0bNgwZWZmuqBKwLW+//573XnnnQoODlZAQIAiIyP1+uuvO8whL8B5u3btUp8+fVS3bl0FBATo2muv1bhx45SVleUwj8ygrMnMzNTYsWPVrVs3BQcHy2azKSEhocC5xX3vkpubq5dffllXX321/Pz81KJFC82dO/cKH8nlKe/qAuC5BgwYoPnz5+vJJ59Uo0aNlJCQoO7du2vVqlXq0KGDq8sDXGbixIlav369evfurRYtWuiPP/7Qm2++qdatW+vbb7+1v9lPTU1Vx44dFRQUpAkTJigzM1OTJk3Stm3blJiYqIoVK7r4SADXSE1N1YQJE1SpUqV867Zu3apbb71VTZs21SuvvKLU1FRNmjRJu3bt0v/+9z8XVAu4xooVKxQdHa1WrVppzJgxqly5svbs2aPU1FT7HPICnJeSkqKoqCgFBQXpiSeeUHBwsDZu3KixY8fqu+++0+LFiyWRGZRNhw8f1rhx41S/fn1dd911Wr16dYHzTN67PPPMM3rppZf0yCOPqG3btlq8eLHuv/9+2Ww29enTx0lHZsgCSmDTpk2WJCsuLs4+durUKSs8PNy68cYbXVgZ4Hrr16+3srOzHcaSk5MtX19fq2/fvvaxxx57zPL397d+++03+9jKlSstSdaMGTOcVi/gbu69917rlltusTp16mRFREQ4rLv99tutsLAw6/jx4/axt99+25JkLV++3NmlAi5x/Phxq2bNmtY//vEPKycnp9B55AU474UXXrAkWdu3b3cY79evnyXJysjIsCyLzKBsOn36tJWenm5ZlmVt3rzZkmTNnj0737zivndJTU21KlSoYA0ZMsQ+lpuba918881W3bp1rXPnzl25g7kMfKwSJTJ//nz5+Pho0KBB9jE/Pz8NHDhQGzduVEpKigurA1yrffv2+a76atSokSIiIvTzzz/bxz799FPdcccdql+/vn3sb3/7mxo3bqx58+Y5rV7Anaxdu1bz58/Xq6++mm/diRMntHLlSj3wwAMKDAy0j/fr10+VK1cmNygz5syZowMHDuiFF15QuXLl9Oeffyo3N9dhDnkB/nLixAlJUs2aNR3Gw8LCVK5cOVWsWJHMoMzy9fVVrVq1LjmvuO9dFi9erLNnz+rxxx+3j9lsNj322GNKTU3Vxo0bS/cASgnNMZRIUlKSGjdu7PCLQ5KioqIknb8kGcBfLMvSgQMHVKNGDUlSWlqaDh48qDZt2uSbGxUVpaSkJGeXCLhcTk6Ohg4dqocffljNmzfPt37btm06d+5cvtxUrFhRLVu2JDcoM7788ksFBgYqLS1NTZo0UeXKlRUYGKjHHntMp0+flkRegAt17txZkjRw4EBt3bpVKSkp+vjjjzVt2jQNGzZMlSpVIjNAEUzeuyQlJalSpUpq2rRpvnl5690RzTGUSHp6usLCwvKN543t37/f2SUBbu3DDz9UWlqa7r33XknnMySp0BxlZGQoOzvbqTUCrjZ9+nT99ttvGj9+fIHrL5UbfvegrNi1a5fOnTunu+66S127dtWnn36qf/7zn5o+fboeeughSeQFuFC3bt00fvx4rVy5Uq1atVL9+vXVp08fDR06VFOmTJFEZoCimLx3SU9PV82aNWWz2fLNk9y3V8AN+VEip06dkq+vb75xPz8/+3oA5+3cuVNDhgzRjTfeqP79+0v6KyOXylFB6wFvdOTIEf3nP//RmDFjFBISUuCcS+WG3z0oKzIzM5WVlaXBgwfbn055991368yZM5oxY4bGjRtHXoCLNGjQQB07dtQ999yj6tWr6/PPP9eECRNUq1YtPfHEE2QGKILJexdP7RXQHEOJ+Pv7F3hVS96l/P7+/s4uCXBLf/zxh3r06KGgoCD7vfqkvzJCjoDznn32WQUHB2vo0KGFzrlUbsgMyoq87/X77rvPYfz+++/XjBkztHHjRgUEBEgiL4AkffTRRxo0aJCSk5NVt25dSecbyrm5uRo5cqTuu+8+fscARTB57+KpvQI+VokSCQsLs19aeaG8sdq1azu7JMDtHD9+XLfffruOHTumZcuWOeQi77LiwnIUHBzMVWMoM3bt2qW33npLw4YN0/79+7Vv3z7t27dPp0+f1tmzZ7Vv3z5lZGRcMjf87kFZkfe9fvHNxUNDQyVJR48eJS/ABeLj49WqVSt7YyzPnXfeqaysLCUlJZEZoAgm713CwsL0xx9/yLKsfPMk9+0V0BxDibRs2VLJycn2J7/k2bRpk309UJadPn1a0dHRSk5O1tKlS9WsWTOH9XXq1FFISIi2bNmSb9vExEQyhDIlLS1Nubm5GjZsmK6++mr7smnTJiUnJ+vqq6/WuHHjFBkZqfLly+fLzZkzZ7R161ZygzLj+uuvl3Q+OxfKu49LSEgIeQEucODAAeXk5OQbP3v2rCTp3LlzZAYogsl7l5YtWyorK0s///yzwzx37xXwsUoXOnbsmNasWaN69ep53BUirVu3Vk5OjsaNG2e/8euZM2c0ffp0NW/eXCdOnNCOHTtcXCXgGjk5OfrXv/6lDRs26I033lBgYGCBeejSpYsWL16sL7/80v5/Y7799lslJyfr3nvvJUMoM8qXL6/XXnst3/jrr7+uP//8U6NGjVK9evWUmpqqdu3aafbs2erVq5cqVaok6fyjxTMzMxUVFUVuUCbkNccmT57scPXY5MmT5ePjo7CwMPICXCAsLEzr16/X559/rgYNGtjHZ8yYIZvNJl9fXzIDSNqzZ4+k8//z5eLv9+K+d7n22mtVvnx5jR8/Xs8++6wkybIsTZ48WaGhoQoKCnJKlrKzs5WSkqJOnTqpatWql97A8gInT560/vOf/1hdu3a1qlWrZkmyZs+eXeztjx49aj3yyCNWjRo1rICAAKtz587Wd999l2/eVVddZUnKtzz66KMlqnvRokUF7o+FhYWFhYWFhYWFhYWFhYWF5fKWRYsWFas/4xVXjh0+fFjjxo1T/fr1dd1112n16tXF3jY3N1c9evTQDz/8oKefflo1atRQfHy8OnfurO+++06NGjVymN+yZUs99dRTDmONGzcuUd316tWTJC1atEgNGzYs0T7cxfTp0zV48GBXlwF4DDIDmCEzgBkyA5ghM4AZd8/M7t271bNnT3vf5VK8ojmWd3P4WrVqacuWLWrbtm2xt50/f742bNigTz75RL169ZIkxcTEqHHjxho7dqzmzJnjML9OnTp64IEHSqXuvI9SNmzYUBEREaWyT1d58MEHPf4YAGciM4AZMgOYITOAGTIDmPGUzBT3FlZecUN+X19f1apVq0Tbzp8/XzVr1tTdd99tHwsJCVFMTIwWL15c4CNIz5w5oz///LPE9Xqji28IC6BoZAYwQ2YAM2QGMENmADPelhmvaI5djqSkJLVu3Vrlyjn+VURFRSkrK0vJyckO419//bUCAgJUuXJlNWjQoMAbCJdFR48edXUJgEchM4AZMgOYITOAGTIDmPG2zHjFxyovR3p6ujp27JhvPO/pC/v371fz5s0lSS1atFCHDh3UpEkTHTlyRAkJCXryySe1f/9+TZw4scjXOXjwoA4dOuQwtnv37lI6Ctcr6O8QQOHIDGCGzABmyAxghswAZrwtM2X+yrFTp04V+BlUPz8/+/o8n332mUaMGKG77rpL//znP7VmzRp17dpVr7zyilJTU4t8nfj4eEVGRjosPXv2lCStW7dOa9asUVxcnDIyMtS/f39JUnR0tCQpNjZWu3fv1qxZs7Rw4UIlJiZq/PjxysrKUkxMjMPc0aNHa9u2bZozZ47mzJmjbdu2afTo0Q5zYmJilJWVpfHjxysxMVELFy7UrFmztHv3bsXGxjrM7d+/vzIyMhQXF6c1a9Zo2bJlmjp1qtLS0uw334uOjtbUqVM1ePBgpaWlaerUqVq2bJnHH5MkjoljumLHNGrUKK87Jm88TxyT+xzTxfvzhmPyxvPEMbnPMXXv3t3rjskbzxPH5D7HNHToUK87Jm88TxyT+xzTq6++6tbHtHnzZpmwWZZlGW3h5vJuyD979mwNGDDgkvMrV66se++9VzNnznQY/+KLL9SjRw8tW7ZMXbt2LXT75cuXq1u3bnr//feLvFF/YVeO9ezZU9u3b/eIG9kBAAAAAAC4ux07digyMrLY/ZYyf+VY3pMuL5Y3Vrt27SK3z3ssaEZGRpHzQkNDFRER4bA0bNiwhFW7n7xuLYDiITOAGTIDmCEzgBkyA5jxtsyU+eZYy5Yt9f333ys3N9dhfNOmTQoICFDjxo2L3H7v3r2Szj/hsixbsmSJq0sAPAqZAcyQGcAMmQHMkBnAjLdlpkw1x9LT07Vz506dPXvWPtarVy8dOHBACxYssI8dPnxYn3zyiaKjo+33I8vIyFBOTo7D/s6ePauXXnpJFStWVJcuXZxzEG4q73PJAIqHzABmyAxghswAZsgMYMbbMuM1T6t88803dezYMe3fv1/S+S5m3k3yhw4dqqCgII0aNUrvvvuufv31VzVo0EDS+ebYDTfcoIceekg//fSTatSoofj4eOXk5Oi5556z7/+zzz7T888/r169eunqq69WRkaG5syZo+3bt2vChAmqVauW04/ZnUyZMsXVJQAehcwAZsgMYIbMAGbIDGDG2zLjNVeOTZo0SWPGjNG0adMkSQsWLNCYMWM0ZswYHT16tNDtfHx89MUXX+jee+/V66+/rqefflo1atTQ119/rSZNmtjnNW/eXM2aNdMHH3ygYcOGacKECapatarmzZunUaNGXfHjc3cXP9AAQNHIDGCGzABmyAxghswAZrwtMy65ciwtLU1r167VwYMHdc8996hu3brKycnR8ePHFRQUJB8fH+N97tu375JzEhISlJCQkG+8WrVqeuedd/TOO+8Uuu3111+vzz77zLiusiIqKsrVJQAehcwAZsgMYIbMAGbIDGDG2zLj1CvHLMvSv//9b1199dXq27ev/v3vfys5OVmSlJmZqQYNGuiNN95wZkkoJadOnXJ1CYBHITOAGTIDmCEzgBkyA5jxtsw4tTkWFxen1157TcOHD9fKlStlWZZ9XVBQkO6++259+umnziwJpWTPnj2uLgHwKGQGMENmADNkBjBDZgAz3pYZpzbH3n77bfXr108TJkxQy5Yt861v0aKF/UoyeJaePXu6ugTAo5AZwAyZAcyQGcAMmQHMeFtmnNocS0lJUfv27QtdX6lSJZ04ccKJFaG0jB8/3tUlAB6FzABmyAxghswAZsgMYMbbMuPU5lhoaKhSUlIKXf/dd9+pfv36TqwIpWX69OmuLgHwKGQGMENmADNkBjBDZgAz3pYZpzbH7r77bk2fPl179+61j9lsNknSihUrlJCQoN69ezuzJJSS6OhoV5cAeBQyA5ghM4AZMgOYITOAGW/LjM268K74V9jx48fVsWNH/frrr7r55pu1bNky/f3vf1dmZqY2btyoVq1aae3atQoICHBWSS61Y8cORUZGavv27YqIiHB1OQAAAAAAAB7PtN/i1CvHgoKC9O2332rEiBFKS0uTn5+f1qxZo2PHjmns2LH65ptvykxjzNsMHjzY1SUAHoXMAGbIDGCGzABmyAxgxtsy49Qrx+DIm64cS0tLU506dVxdBuAxyAxghswAZsgMYIbMAGbcPTNufeXYuXPninwa5YkTJ3Tu3DknVoTSsmjRIleXAHgUMgOYITOAGTIDmCEzgBlvy4xTm2PDhg1T+/btC11/00036amnnnJiRSgt4eHhri4B8ChkBjBDZgAzZAYwQ2YAM96WGac2x5YtW6ZevXoVur5Xr1764osvnFgRSou/v7+rSwA8CpkBzJAZwAyZAcyQGcCMt2XGqc2x/fv3F/mZ1Nq1aystLc2JFaG0JCYmuroEwKOQGcAMmQHMkBnADJkBzHhbZpzaHKtevbp++eWXQtf//PPPCgwMdGJFKC0DBw50dQmARyEzgBkyA5ghM4AZMgOY8bbMOLU51q1bN82YMUNJSUn51n3//fd66623dPvttzuzJJSS2NhYV5cAeBQyA5ghM4AZMgOYITOAGW/LjM2yLMtZL7Z//361bdtWBw8e1J133ml/nOb27du1ZMkShYaGatOmTapbt66zSnIp00eLAgAAAAAAoGim/RanXjlWu3ZtbdmyRffff7+++uorPf/883r++ef19ddfq2/fvtq8eXOZaYx5m+joaFeXAHgUMgOYITOAGTIDmCEzgBlvy4xTrxy7kGVZOnTokCQpJCRENpvNFWW4FFeOAQAAAAAAlC63vnLsQjabTaGhoQoNDS2TjTFv422fNwauNDIDmCEzgBkyA5ghM4AZb8tMeWe/4NGjRzV37lzt3btXR48e1cUXrtlsNs2cOdPZZeEyDRkyxNUlAB6FzABmyAxghswAZsgMYMbbMuPU5tjy5cvVq1cv/fnnnwoMDFS1atXyzeEqMs+0du1aNWzY0NVlAB6DzABmyAxghswAZsgMYMbbMuPU5thTTz2lWrVqacGCBWrevLkzXxpXWEGNTgCFIzOAGTIDmCEzgBkyA5jxtsw49Z5ju3fv1rBhw2iMeaE6deq4ugTAo5AZwAyZAcyQGcAMmQHMeFtmnNoca9SokU6ePOnMl4STLF++3NUlAB6FzABmyAxghswAZsgMYMbbMmOzLr4j/hW0ePFiDRkyROvWrVODBg2c9bJuy/TRou4sKytLAQEBri4D8BhkBjBDZgAzZAYwQ2YAM+6eGdN+i1PvOfbVV18pJCRETZs21d///nfVq1dPPj4+DnNsNptee+01Z5aFUjBgwADNmzfP1WUAHoPMAGbIDGCGzABmyAxgxtsy49Qrx8qVu/SnOG02m3JycpxQjet505VjAAAAAAAA7sC03+LUe47l5uZecikrjTFvEx0d7eoSAI9CZgAzZAYwQ2YAM2QGMONtmXHqlWNwxJVjAAAAAAAApcutrxzL8+233+rFF19UbGysdu3aJen8zdy+//57ZWZmuqIkXKbRo0e7ugTAo5AZwAyZAcyQGcAMmQHMeFtmnNocO3PmjO6++27ddNNNeuaZZ/T6668rJSXlfCHlyum2227jZvwe6r777nN1CYBHITOAGTIDmCEzgBkyA5jxtsw4tTk2ZswYLV26VNOmTdMvv/yiCz/R6efnp969e2vx4sXOLAmlZNu2ba4uAfAoZAYwQ2YAM2QGMENmADPelhmnNsfmzp2rxx57TIMGDVJwcHC+9U2bNtXevXudWRIAAAAAAADKMKc2xw4ePKjmzZsXut7Hx0dZWVlOrAilpajzCiA/MgOYITOAGTIDmCEzgBlvy4xTm2P16tXTzp07C12/fv16NWzY0IkVobTMnTvX1SUAHoXMAGbIDGCGzABmyAxgxtsy49Tm2P33368ZM2Zo48aN9jGbzSZJevvttzVv3jz169fPmSWhlEyYMMHVJQAehcwAZsgMYIbMAGbIDGDG2zLj1ObYM888o/bt26tjx47q0qWLbDabYmNjVb9+fT366KPq1q2bYmNjnVkSSkl0dLSrSwA8CpkBzJAZwAyZAcyQGcCMt2XGZl34yEgnsCxLH374oebPn69du3YpNzdX4eHhiomJ0YMPPmi/kqws2LFjhyIjI7V9+3ZFRES4uhwAAAAAAACPZ9pvceqVY9L5j1E+8MADWrRokXbs2KGff/5ZS5cuVb9+/cpUY8zbxMTEuLoEwKOQGcAMmQHMkBnADJkBzHhbZpx+5Rj+4k1XjmVlZSkgIMDVZQAeg8wAZsgMYIbMAGbIDGDG3TPj9leOLV++XDExMWrTpo3Cw8N1zTXXOCzh4eHOLgmlYPLkya4uAfAoZAYwQ2YAM2QGMENmADPelhmnNsfi4uLUvXt3rVu3TnXr1lXHjh3VqVMnh6Vjx47G+83MzNTYsWPVrVs3BQcHy2azKSEhodjbHzt2TIMGDVJISIgqVaqkLl266Pvvvy9w7meffabWrVvLz89P9evX19ixY3Xu3Dnjmr1N165dXV0C4FHIDGCGzABmyAxghswAZrwtM+Wd+WKvvfaabrnlFn3xxReqUKFCqe338OHDGjdunOrXr6/rrrtOq1evLva2ubm56tGjh3744Qc9/fTTqlGjhuLj49W5c2d99913atSokX3u//73P/Xs2VOdO3fWG2+8oW3btun555/XwYMHNW3atFI7Hk+Ulpbm6hIAj0JmADNkBjBDZgAzZAYw422ZcWpz7OjRo+rVq1epNsYkKSwsTOnp6apVq5a2bNmitm3bFnvb+fPna8OGDfrkk0/Uq1cvSedvLNe4cWONHTtWc+bMsc8dPny4WrRooRUrVqh8+fN/dYGBgZowYYL+9a9/6dprry3V4/IkR48edXUJgEchM4AZMgOYITOAGTIDmPG2zDj1Y5VRUVH65ZdfSn2/vr6+qlWrVom2nT9/vmrWrKm7777bPhYSEqKYmBgtXrxY2dnZkqSffvpJP/30kwYNGmRvjEnS448/LsuyNH/+/Ms7CA9Xko/DAmUZmQHMkBnADJkBzJAZwIy3ZcapV47Fx8fr9ttvV5s2bXT//fc786ULlZSUpNatW6tcOcc+YVRUlN566y0lJyerefPmSkpKkiS1adPGYV7t2rVVt25d+/rCHDx4UIcOHXIY2717dykcges9t2SHlnyTpPDwQ5eeDECStGfPHjIDGCAzgBkyA5ghM8BfmtUO1Njoop/wOHXqVE2ZMsVJFV15Tm2O3XvvvTp37pwefPBBPfbYY6pbt658fHwc5thsNv3www9Oqyk9Pb3AjmdYWJgkaf/+/WrevLnS09Mdxi+eu3///iJfJz4+Xs8991yB69atW6fDhw8rMTFRAwcOVGxsrN59911FR0dryZIlio2N1ZAhQ7R27VpVq1ZNderU0fLly/XUU09pwIABmjdvnn3u6NGjdd9992nbtm2SpObNm2vu3LmaMGGCfU5MTIwSEhI0efJkde3aVWlpaTp69Kg6duxo/wbPm9u/f39NmTJFM2fOVFRUlE6dOqU9e/aoZ8+eGj9+vKZPn655Kzboz0q1dfjXDKO/e6BMK1eNzAAmyAxghswAZsgM4OBS/YgXXnhBMTExLu9H5M0dPHiwxowZo0WLFik8PFx//PGH0fHaLMuyrsRfZEE6d+4sm812yXmrBLWKWAAAJHJJREFUVq0q8Wvk3XNs9uzZGjBgwCXn+/j46NFHH1V8fLzD+Ndff61bb71VCxcutP/F/+c//9GBAwcUGhrqMLdjx446ceKEtm7dWujrFHblWM+ePbV9+3ZFRBTdlXVnzy3ZoXkrNigyMtLVpQAeY/v27WQGMEBmADNkBjBDZoC/FOfKsbymlLvasWOHIiMji91vceqVYyZPkXQWf39/+33FLnT69Gn7+gv/W9jcvPWFCQ0NzddU8xZjoyMuGRwAF7vR1QUAHobMAGbIDGCGzAAm3LkxVhJOvSG/O8p70uXF8sZq165tn3fh+MVz8+aVVf3793d1CYBHITOAGTIDmCEzgBkyA5jxtsw49cqxPGfPntXOnTt1/Phx5ebm5lvvzKcetGzZUt98841yc3Mdbsq/adMmBQQEqHHjxvZ50vmPbUZFRdnn7d+/X6mpqRo0aJDTanZH3nQjPsAZyAxghswAZsgMYIbMAGa8LTNOvXIsNzdXI0eOVHBwsFq2bKlOnTqpS5cu+ZYrJT09XTt37tTZs2ftY7169dKBAwe0YMEC+9jhw4f1ySefKDo6Wr6+vpKkiIgIXXvttXrrrbeUk5Njnztt2jTZbDb16tXritXtCWbOnOnqEgCPQmYAM2QGMENmADNkBjDjbZlx6pVjEyZMUFxcnB599FF16NBBDz74oCZOnKiqVasqPj5eNptNL7/8con2/eabb+rYsWP2p0YuWbJEqampkqShQ4cqKChIo0aN0rvvvqtff/1VDRo0kHS+OXbDDTfooYce0k8//aQaNWooPj5eOTk5+Z4uGRcXpzvvvFO33Xab+vTpo+3bt+vNN9/Uww8/rKZNm5b8L8YLXHg1HYBLIzOAGTIDmCEzgBkyA5jxtsw4tTmWkJCgmJgYTZs2TUeOHJEkXX/99brlllvUv39/3Xjjjfr666/1t7/9zXjfkyZN0m+//Wb/esGCBfarwR544AEFBQUVuJ2Pj4+++OILPf3003r99dd16tQptW3bVgkJCWrSpInD3DvuuEMLFizQc889p6FDhyokJESjR4/Wf/7zH+N6vc2pU6dcXQLgUcgMYIbMAGbIDGCGzABmvC0zTv1YZWpqqm655RZJsn9cMe+pkBUrVtQDDzyg999/v0T73rdvnyzLKnDJu0osISHB4es81apV0zvvvKPDhw/rzz//1OrVq9WmTZsCX6dnz55KSkrS6dOnlZKSovHjx6tChQolqtmb7Nmzx9UlAB6FzABmyAxghswAZsgMYMbbMuPU5lj16tWVmZkpSapcubICAwO1d+9ehzlHjx51ZkkoJT179nR1CYBHITOAGTIDmCEzgBkyA5jxtsw4tTnWqlUrbd682f51ly5d9Oqrr2r9+vX65ptv9Prrr+u6665zZkkoJePHj3d1CYBHITOAGTIDmCEzgBkyA5jxtszYLMuynPVin332mRISEjR37lz5+vrqp59+UseOHXX06FFZlqVq1arp888/1w033OCsklxqx44dioyM1Pbt2xUREeHqcgAAAAAAADyeab/FqVeO3XnnnVqwYIH9fmPNmjXTnj17tGDBAn322WfatWtXmWmMeZvo6GhXlwB4FDIDmCEzgBkyA5ghM4AZb8uMU68cgyOuHAMAAAAAAChdbn3lWJ6zZ89q3759SkpK0vfff59vgecZPHiwq0sAPAqZAcyQGcAMmQHMkBnAjLdlxqlXjh07dkzDhw/Xhx9+qDNnzuRbb1mWbDabcnJynFWSS3nTlWNpaWmqU6eOq8sAPAaZAcyQGcAMmQHMkBnAjLtnxrTfUt4JNdkNGDBAS5YsUZ8+fdSuXTsFBQU58+VxBS1atEhDhgxxdRmAxyAzgBkyA5ghM4AZMgOY8bbMOLU5tmLFCg0bNkxTpkxx5svCCcLDw11dAuBRyAxghswAZsgMYIbMAGa8LTNOvedY9erV1bBhQ2e+JJzE39/f1SUAHoXMAGbIDGCGzABmyAxgxtsy49Tm2KBBg/TRRx8pNzfXmS8LJ0hMTHR1CYBHITOAGTIDmCEzgBkyA5jxtsw49Yb8kvTss8/qiy++0IMPPqi6devKx8cn35y7777bmSW5jDfdkD8jI0PBwcGuLgPwGGQGMENmADNkBjBDZgAz7p4Zt74hf1pamr7++mtt3bpVW7duLXBOWXpapTeJjY3Vu+++6+oyAI9BZgAzZAYwQ2YAM2QGMONtmXHqlWNdu3bVmjVr9NRTTxX5tMpOnTo5qySX8qYrxwAAAAAAANyBab/FqfccW7dunUaOHKkXXnhBd955pzp16lTgAs8THR3t6hIAj0JmADNkBjBDZgAzZAYw422ZcWpzrFatWm79mVSU3JIlS1xdAuBRyAxghswAZsgMYIbMAGa8LTNObY499dRTeuedd5SZmenMl4UTxMbGuroEwKOQGcAMmQHMkBnADJkBzHhbZpx6Q/7Tp0+rQoUKatiwoWJiYlSvXr18T6u02Wxe95dcFgwZMsTVJQAehcwAZsgMYIbMAGbIDGDG2zLj1ObY8OHD7X9+8803C5xDc8wzrV27Vg0bNnR1GYDHIDOAGTIDmCEzgBkyA5jxtsw4tTn266+/OvPl4ETVqlVzdQmARyEzgBkyA5ghM4AZMgOY8bbMOK05durUKb322mvq0qWL1z3VAFKdOnVcXQLgUcgMYIbMAGbIDGCGzABmvC0zTrshv7+/v2bMmKEDBw446yXhRMuXL3d1CYBHITOAGTIDmCEzgBkyA5jxtszYLMuynPViHTt2VOvWrfXqq6866yXd2o4dOxQZGant27crIiLC1eVclqysLAUEBLi6DMBjkBnADJkBzJAZwAyZAcy4e2ZM+y1Ou3JMkl599VV99NFHeuedd3Tu3DlnvjSusAEDBri6BMCjkBnADJkBzJAZwAyZAcx4W2aceuVYixYtdPjwYR04cEC+vr6qU6eO/P39HQuy2fTDDz84qySX8qYrxwAAAAAAANyBW185FhwcrCZNmqhjx45q166d6tatq+rVqzsswcHBziwJpYSHLABmyAxghswAZsgMYIbMAGa8LTNOvXIMjrhyDAAAAAAAoHS59ZVj8F6jR492dQmARyEzgBkyA5ghM4AZMgOY8bbMOL05duLECb300kvq2rWrWrVqpcTERElSRkaGXnnlFe3evdvZJaEU3Hfffa4uAfAoZAYwQ2YAM2QGMENmADPelhmnNsdSU1PVqlUr/ec//1Fqaqp+/PFHZWZmSjp/P7IZM2bojTfecGZJKCXbtm1zdQmARyEzgBkyA5ghM4AZMgOY8bbMlHfmiz399NM6efKktm7dqtDQUIWGhjqs79mzp5YuXerMkgAAAAAAAFCGOfXKsRUrVmjYsGFq1qyZbDZbvvXXXHONUlJSnFkSSknz5s1dXQLgUcgMYIbMAGbIDGCGzABmvC0zTm2OnTp1SiEhIYWuP3nypBOrQWmaO3euq0sAPAqZAcyQGcAMmQHMkBnAjLdlxmZZluWsF2vTpo2aNGmiDz/8UEeOHFFISIi+/PJL3XLLLZKkDh06yMfHR2vWrHFWSS5l+mhRAAAAAAAAFM203+LUK8eefPJJffTRR5o4caKOHz8uScrNzdXu3bv14IMPauPGjYqNjXVmSSgl0dHRri4B8ChkBjBDZgAzZAYwQ2YAM96WGadeOSZJL7zwgv773//Ksizl5uaqXLlysixL5cqV0/PPP6+RI0c6sxyX4soxAAAAAACA0uWWV46dPn1aH3/8sV566SWFhoZqz549mjRpkh577DE98sgjmjhxon755Zcy1RjzNjExMa4uAfAoZAYwQ2YAM2QGMENmADPelpkrfuXYwYMH1b59e/3666+yLEs2m00BAQFasGCB/v73v1/Jl3Z73nTlWFZWlgICAlxdBuAxyAxghswAZsgMYIbMAGbcPTNud+XY+PHjtW/fPsXGxmrp0qWaMmWK/Pz8NHjw4Cv90nCiyZMnu7oEwKOQGcAMmQHMkBnADJkBzHhbZspf6RdYsWKF+vXrp0mTJtnHatasqfvvv1+//PKLmjRpcqVLgBN07drV1SUAHoXMAGbIDGCGzABmyAxgxtsyc8WvHPv999/VoUMHh7EOHTrIsiwdOHDgSr88nCQtLc3VJQAehcwAZsgMYIbMAGbIDGDG2zJzxZtj2dnZ8vPzcxjL+/rcuXOl9hojR45U7dq15e/vr3bt2mnlypXF2vajjz5S69at5efnp5CQEA0cOFCHDx/ON89msxW4vPTSS6VyDJ7u6NGjri4B8ChkBjBDZgAzZAYwQ2YAM96WmSv+sUpJ2rdvn77//nv718ePH5ck7dq1S1WrVs03v3Xr1kb7HzBggObPn68nn3xSjRo1UkJCgrp3765Vq1blu2rtQtOmTdPjjz+uW2+9Va+88opSU1P12muvacuWLdq0aVO+pt7f//539evXz2GsVatWRrV6q44dO7q6BMCjkBnADJkBzJAZwAyZAcx4W2au+NMqy5UrJ5vNlm8878mVBY3l5OQUe/+JiYlq166d4uLiNHz4cEnS6dOnFRkZqdDQUG3YsKHA7c6cOaOaNWuqRYsWWr16tb2WpUuXKjo6Wq+//rqGDh1qn2+z2TRkyBC9+eabxa7tUrzpaZWxsbGaMmWKq8sAPAaZAcyQGcAMmQHMkBnAjLtnxrTfcsWvHJs9e/YV3f/8+fPl4+OjQYMG2cf8/Pw0cOBAjR49WikpKapXr16+7bZv365jx47p3nvvdWjS3XHHHapcubI++ugjh+ZYnlOnTslms+W7qqysc+dQAO6IzABmyAxghswAZsgMYMbbMnPFm2P9+/e/ovtPSkpS48aNFRgY6DAeFRUlSdq6dWuBzbHs7GxJkr+/f751/v7+SkpKUm5ursqV++u2bAkJCYqPj5dlWWratKmeffZZ3X///cWq8+DBgzp06JDD2O7du4u1rSeIjo7WkiVLXF0G4DHIDGCGzABmyAxghswAZrwtM1f8hvxXWnp6usLCwvKN543t37+/wO0aNWokm82m9evXO4z/8ssvOnTokE6dOuVwg7n27dvrhRde0KJFizRt2jT5+Piob9++mjZtWrHqjI+PV2RkpMPSs2dPSdK6deu0Zs0axcXFKSMjw95QjI6OlnT+csXdu3dr1qxZWrhwoRITEzV+/HhlZWUpJibGYe7o0aO1bds2zZkzR3PmzNG2bds0evRohzkxMTHKysrS+PHjlZiYqIULF2rWrFnavXu3YmNjHeb2799fGRkZiouL05o1a7Rs2TJNnTpVaWlpGjx4sH3ukiVLNHjwYKWlpWnq1KlatmyZxx+TJI6JY7pixzR8+HCvOyZvPE8ck/scU8OGDb3umLzxPHFM7nNM/v7+XndM3nieOCb3Oab77rvP647JG88Tx+Q+x/Txxx+79TFt3rxZJq74PceutPDwcDVp0kRffPGFw/jevXsVHh6uKVOm6Mknnyxw2z59+ujTTz/VSy+9pH/84x9KS0vT0KFD9dNPP+ns2bNKSUlR3bp1C9z2zJkzuv7665Wamqr9+/cXeAXahQq7cqxnz55ecc+x/v37691333V1GYDHIDOAGTIDmCEzgBkyA5hx98yY3nPM468c8/f3t39E8kKnT5+2ry/MjBkz1L17dw0fPlzh4eHq2LGjmjdvbu88Vq5cudBtK1asqCeeeELHjh3Td999d8k6Q0NDFRER4bDk/V9wb+BtnzcGrjQyA5ghM4AZMgOYITOAGW/LjMc3x8LCwpSenp5vPG+sdu3ahW4bFBSkxYsX67ffftOaNWu0b98+vf/++0pPT1dISIiqVq1a5Gvn3cssIyOj5AfgJWbOnOnqEgCPQmYAM2QGMENmADNkBjDjbZm54jfkv9JatmypVatW6cSJEw435d+0aZN9/aXUr19f9evXlyT7lWD33HPPJbfbu3evJCkkJKQElXuXvAcgACgeMgOYITOAGTIDmCEzgBlvy4zHN8d69eqlSZMm6a233tLw4cMlnX8S5ezZs9WuXTv71V2///67srKydO211xa5v1GjRuncuXP2G8FJ0qFDh/I1wE6ePKlXX31VNWrU0PXXX1+i2vM+DuoNT63cuXOnatSo4eoyAI9BZgAzZAYwQ2YAM2QGMOPumcnrsxR0G66CeHxzrF27durdu7dGjRqlgwcPqmHDhnr33Xe1b98+h8v8+vXrpzVr1ujC5w+89NJL2r59u9q1a6fy5ctr0aJFWrFihZ5//nm1bdvWPm/q1KlatGiRoqOjVb9+faWnp2vWrFn6/fff9f7776tixYolqj0lJUWS7E+tBAAAAAAAQOlISUlR69atLznP45tjkvTee+9pzJgxev/993X06FG1aNFCS5cuVceOHYvcrnnz5lq4cKE+++wz5eTkqEWLFpo3b5569+7tMO+mm27Shg0b9M477+jIkSOqVKmSoqKiNGvWLN1yyy0lrrtTp05atGiR6tWrJ19f3xLvx9Xynrq5aNEir3rIAHClkBnADJkBzJAZwAyZAcx4Qmays7OVkpKiTp06FWu+zbrwUiqgBEwfkQqUdWQGMENmADNkBjBDZgAz3pgZj39aJQAAAAAAAFBSNMcAAAAAAABQZtEcAwAAAAAAQJlFcwyXLSQkRGPHjlVISIirSwE8ApkBzJAZwAyZAcyQGcCMN2aGG/IDAAAAAACgzOLKMQAAAAAAAJRZNMcAAAAAAABQZtEcAwAAAAAAQJlFcwwAAAAAAABlFs0xAAAAAAAAlFk0x1Bi2dnZGjlypGrXri1/f3+1a9dOK1eudHVZgMtt3rxZTzzxhCIiIlSpUiXVr19fMTExSk5Ozjf3559/Vrdu3VS5cmUFBwfrwQcf1KFDh1xQNeBeXnjhBdlsNkVGRuZbt2HDBnXo0EEBAQGqVauWhg0bpszMTBdUCbjW999/rzvvvFPBwcEKCAhQZGSkXn/9dYc55AU4b9euXerTp4/q1q2rgIAAXXvttRo3bpyysrIc5pEZlDWZmZkaO3asunXrpuDgYNlsNiUkJBQ4t7jvXXJzc/Xyyy/r6quvlp+fn1q0aKG5c+de4SO5POVdXQA814ABAzR//nw9+eSTatSokRISEtS9e3etWrVKHTp0cHV5gMtMnDhR69evV+/evdWiRQv98ccfevPNN9W6dWt9++239jf7qamp6tixo4KCgjRhwgRlZmZq0qRJ2rZtmxITE1WxYkUXHwngGqmpqZowYYIqVaqUb93WrVt16623qmnTpnrllVeUmpqqSZMmadeuXfrf//7ngmoB11ixYoWio6PVqlUrjRkzRpUrV9aePXuUmppqn0NegPNSUlIUFRWloKAgPfHEEwoODtbGjRs1duxYfffdd1q8eLEkMoOy6fDhwxo3bpzq16+v6667TqtXry5wnsl7l2eeeUYvvfSSHnnkEbVt21aLFy/W/fffL5vNpj59+jjpyAxZQAls2rTJkmTFxcXZx06dOmWFh4dbN954owsrA1xv/fr1VnZ2tsNYcnKy5evra/Xt29c+9thjj1n+/v7Wb7/9Zh9buXKlJcmaMWOG0+oF3M29995r3XLLLVanTp2siIgIh3W33367FRYWZh0/ftw+9vbbb1uSrOXLlzu7VMAljh8/btWsWdP6xz/+YeXk5BQ6j7wA573wwguWJGv79u0O4/369bMkWRkZGZZlkRmUTadPn7bS09Mty7KszZs3W5Ks2bNn55tX3PcuqampVoUKFawhQ4bYx3Jzc62bb77Zqlu3rnXu3LkrdzCXgY9VokTmz58vHx8fDRo0yD7m5+engQMHauPGjUpJSXFhdYBrtW/fPt9VX40aNVJERIR+/vln+9inn36qO+64Q/Xr17eP/e1vf1Pjxo01b948p9ULuJO1a9dq/vz5evXVV/OtO3HihFauXKkHHnhAgYGB9vF+/fqpcuXK5AZlxpw5c3TgwAG98MILKleunP7880/l5uY6zCEvwF9OnDghSapZs6bDeFhYmMqVK6eKFSuSGZRZvr6+qlWr1iXnFfe9y+LFi3X27Fk9/vjj9jGbzabHHntMqamp2rhxY+keQCmhOYYSSUpKUuPGjR1+cUhSVFSUpPOXJAP4i2VZOnDggGrUqCFJSktL08GDB9WmTZt8c6OiopSUlOTsEgGXy8nJ0dChQ/Xwww+refPm+dZv27ZN586dy5ebihUrqmXLluQGZcaXX36pwMBApaWlqUmTJqpcubICAwP12GOP6fTp05LIC3Chzp07S5IGDhyorVu3KiUlRR9//LGmTZumYcOGqVKlSmQGKILJe5ekpCRVqlRJTZs2zTcvb707ojmGEklPT1dYWFi+8byx/fv3O7skwK19+OGHSktL07333ivpfIYkFZqjjIwMZWdnO7VGwNWmT5+u3377TePHjy9w/aVyw+8elBW7du3SuXPndNddd6lr16769NNP9c9//lPTp0/XQw89JIm8ABfq1q2bxo8fr5UrV6pVq1aqX7+++vTpo6FDh2rKlCmSyAxQFJP3Lunp6apZs6ZsNlu+eZL79gq4IT9K5NSpU/L19c037ufnZ18P4LydO3dqyJAhuvHGG9W/f39Jf2XkUjkqaD3gjY4cOaL//Oc/GjNmjEJCQgqcc6nc8LsHZUVmZqaysrI0ePBg+9Mp7777bp05c0YzZszQuHHjyAtwkQYNGqhjx4665557VL16dX3++eeaMGGCatWqpSeeeILMAEUwee/iqb0CmmMoEX9//wKvasm7lN/f39/ZJQFu6Y8//lCPHj0UFBRkv1ef9FdGyBFw3rPPPqvg4GANHTq00DmXyg2ZQVmR971+3333OYzff//9mjFjhjZu3KiAgABJ5AWQpI8++kiDBg1ScnKy6tatK+l8Qzk3N1cjR47Ufffdx+8YoAgm7108tVfAxypRImFhYfZLKy+UN1a7dm1nlwS4nePHj+v222/XsWPHtGzZModc5F1WXFiOgoODuWoMZcauXbv01ltvadiwYdq/f7/27dunffv26fTp0zp79qz27dunjIyMS+aG3z0oK/K+1y++uXhoaKgk6ejRo+QFuEB8fLxatWplb4zlufPOO5WVlaWkpCQyAxTB5L1LWFiY/vjjD1mWlW+e5L69AppjKJGWLVsqOTnZ/uSXPJs2bbKvB8qy06dPKzo6WsnJyVq6dKmaNWvmsL5OnToKCQnRli1b8m2bmJhIhlCmpKWlKTc3V8OGDdPVV19tXzZt2qTk5GRdffXVGjdunCIjI1W+fPl8uTlz5oy2bt1KblBmXH/99ZLOZ+dCefdxCQkJIS/ABQ4cOKCcnJx842fPnpUknTt3jswARTB579KyZUtlZWXp559/dpjn7r0CmmMokV69eiknJ0dvvfWWfSw7O1uzZ89Wu3btVK9ePRdWB7hWTk6O7r33Xm3cuFGffPKJbrzxxgLn3XPPPVq6dKlSUlLsY1999ZWSk5PVu3dvZ5ULuFxkZKQWLlyYb4mIiFD9+vW1cOFCDRw4UEFBQfrb3/6mDz74QCdPnrRv//777yszM5PcoMyIiYmRJM2cOdNh/J133lH58uXVuXNn8gJcoHHjxkpKSlJycrLD+Ny5c1WuXDm1aNGCzACXUNz3LnfddZcqVKig+Ph4+5hlWZo+fbrq1Kmj9u3bO7Xu4rJZF1/rBhRTTEyMFi5cqNjYWDVs2FDvvvuuEhMT9dVXX6ljx46uLg9wmSeffFKvvfaaoqOj7W9gLvTAAw9IklJSUtSqVStVrVpV//rXv5SZmam4uDjVrVtXmzdv5mOVKPM6d+6sw4cPa/v27fax77//Xu3bt1ezZs00aNAgpaamavLkyerYsaOWL1/uwmoB5xo4cKBmzZqlmJgYderUSatXr9Ynn3yiUaNGacKECZLIC5Bn7dq1uuWWW1S9enU98cQTql69upYuXar//e9/evjhh/X2229LIjMou958800dO3ZM+/fv17Rp03T33XerVatWkqShQ4cqKCjI6L3LiBEjFBcXp0GDBqlt27ZatGiRPv/8c3344Ye6//77XXWYRbOAEjp16pQ1fPhwq1atWpavr6/Vtm1ba9myZa4uC3C5Tp06WZIKXS60fft267bbbrMCAgKsqlWrWn379rX++OMPF1UOuJdOnTpZERER+ca/+eYbq3379pafn58VEhJiDRkyxDpx4oQLKgRc58yZM9Z///tf66qrrrIqVKhgNWzY0JoyZUq+eeQFOG/Tpk3W7bffbtWqVcuqUKGC1bhxY+uFF16wzp496zCPzKAsuuqqqwp97/Lrr7/a5xX3vUtOTo41YcIE66qrrrIqVqxoRUREWB988IETj8gcV44BAAAAAACgzOKeYwAAAAAAACizaI4BAAAAAACgzKI5BgAAAAAAgDKL5hgAAAAAAADKLJpjAAAAAAAAKLNojgEAAAAAAKDMojkGAAAAAACAMovmGAAAAAAAAMosmmMAAAAAAAAos2iOAQAAAAAAoMyiOQYAAOBBBgwYoAYNGri6DLv//ve/stlsstlsqly5stNfv2XLlvbXv+OOO5z++gAAwPOVd3UBAAAAZZ3NZivWvFWrVl3hSkru/fffV4UKFZz+uhMmTFBGRoZiY2Od/toAAMA70BwDAABwsffff9/h6/fee08rV67MN960aVO9/fbbys3NdWZ5xfLAAw+45HW7d+8uSXr22Wdd8voAAMDz0RwDAABwsYsbS99++61WrlzpsoYTAABAWcI9xwAAADzIxfcc27dvn2w2myZNmqSpU6fqmmuuUUBAgG677TalpKTIsiyNHz9edevWlb+/v+666y5lZGTk2+///vc/3XzzzapUqZKqVKmiHj16aMeOHZdVa4MGDXTHHXdo9erVatOmjfz9/dW8eXOtXr1akrRgwQI1b95cfn5+uv7665WUlOSw/R9//KGHHnpIdevWla+vr8LCwnTXXXdp3759l1UXAADAhbhyDAAAwAt8+OGHOnPmjIYOHaqMjAy9/PLLiomJ0S233KLVq1dr5MiR2r17t9544w0NHz5cs2bNsm/7/vvvq3///uratasmTpyorKwsTZs2TR06dFBSUtJlPQBg9+7duv/++/Xoo4/qgQce0KRJkxQdHa3p06dr9OjRevzxxyVJL774omJiYvTLL7+oXLnz///2nnvu0Y4dOzR06FA1aNBABw8e1MqVK/X777+71UMJAACAZ6M5BgAA4AXS0tK0a9cuBQUFSZJycnL04osv6tSpU9qyZYvKlz//z75Dhw7pww8/1LRp0+Tr66vMzEwNGzZMDz/8sN566y37/vr3768mTZpowoQJDuOmfvnlF23YsEE33nijJKlZs2bq2rWrHnnkEe3cuVP169eXJFWrVk2PPvqo1q5dq86dO+vYsWPasGGD4uLiNHz4cPv+Ro0aVeJaAAAACsLHKgEAALxA79697Y0xSWrXrp2k8/czy2uM5Y2fOXNGaWlpkqSVK1fq2LFjuu+++3T48GH74uPjo3bt2l32EzKbNWtmb4xdWNctt9xib4xdOL53715Jkr+/vypWrKjVq1fr6NGjl1UDAABAUbhyDAAAwAtc2GiSZG+U1atXr8DxvIbTrl27JJ1vVhUkMDDQJXX5+vpq4sSJeuqpp1SzZk3dcMMNuuOOO9SvXz/VqlXrsmoCAAC4EM0xAAAAL+Dj42M0blmWJCk3N1fS+fuOFdR0uvCqM2fWJUlPPvmkoqOjtWjRIi1fvlxjxozRiy++qK+//lqtWrW6rLoAAADy0BwDAAAow8LDwyVJoaGh+tvf/ubiavILDw/XU089paeeekq7du1Sy5YtNXnyZH3wwQeuLg0AAHgJ7jkGAABQhnXt2lWBgYGaMGGCzp49m2/9oUOHXFCVlJWVpdOnTzuMhYeHq0qVKsrOznZJTQAAwDtx5RgAAEAZFhgYqGnTpunBBx9U69at1adPH4WEhOj333/X559/rptuuklvvvmm0+tKTk7WrbfeqpiYGDVr1kzly5fXwoULdeDAAfXp08fp9QAAAO9FcwwAAKCMu//++1W7dm299NJLiouLU3Z2turUqaObb75ZDz30kEtqqlevnu677z599dVXev/991W+fHlde+21mjdvnu655x6X1AQAALyTzbrwrqcAAACAgf/+97967rnndOjQIdlsNlWvXt2pr3/s2DGdO3dOrVu3VosWLbR06VKnvj4AAPB83HMMAAAAly0kJERXXXWV01+3c+fOCgkJUUpKitNfGwAAeAeuHAMAAECJ7d27V3v37pUklS9fXp07d3bq62/atEknT56UdL5Bd9111zn19QEAgOejOQYAAAAAAIAyi49VAgAAAAAAoMyiOQYAAAAAAIAyi+YYAAAAAAAAyiyaYwAAAAAAACizaI4BAAAAAACgzKI5BgAAAAAAgDKL5hgAAAAAAADKLJpjAAAAAAAAKLNojgEAAAAAAKDMojkGAAAAAACAMovmGAAAAAAAAMqs/wcZo7yLZf71LQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def simulate_stdsp_dependence_on_third_factor(params, z):\n",
+    "    \n",
+    "    nest.ResetKernel()\n",
+    "    nest.Install(module_name)\n",
+    "    nest.set_verbosity(nest_verbosity)\n",
+    "    nest.SetKernelStatus({\n",
+    "        'resolution': params['dt'],\n",
+    "        'print_time': False,\n",
+    "        'local_num_threads': n_threads,\n",
+    "        'rng_seed': params['seed']\n",
+    "    })\n",
+    "    \n",
+    "    neuron_1 = nest.Create(params['soma_model'], params=params['soma_params'])\n",
+    "    neuron_2 = nest.Create(params['soma_model'], params=params['soma_params'])\n",
+    "    \n",
+    "    # connect two neurons\n",
+    "    nest.Connect(neuron_1, neuron_2, syn_spec=params['syn_dict_ee'])\n",
+    "    \n",
+    "    # creation of spike generator\n",
+    "    time_neuron_1 = 10.\n",
+    "    time_neuron_2 = time_neuron_1 + params['DeltaT']\n",
+    "    \n",
+    "    training_steps = 100\n",
+    "    between_exc = 5*params['DeltaT']\n",
+    "    \n",
+    "    times_neuron_1 = [time_neuron_1+i*between_exc for i in range(training_steps)]\n",
+    "    times_neuron_2 = [time_neuron_2+i*between_exc for i in range(training_steps)]#[:10]\n",
+    "    \n",
+    "    # create the spike generators \n",
+    "    # disable spike generator for the interval 'dis', to see the affect of stpd\n",
+    "    dis = 20\n",
+    "    spike_generator_1 = nest.Create('spike_generator', params={'spike_times': times_neuron_1})\n",
+    "    spike_generator_2 = nest.Create('spike_generator', params={'spike_times': times_neuron_2})\n",
+    "    \n",
+    "    # connect the spike generator \n",
+    "    \n",
+    "    params['R_m_soma'] = params['soma_params']['tau_m'] / params['soma_params']['C_m']\n",
+    "    params['syn_dict_ex']['weight'] = psp_max_2_psc_max(params['J_EX_psp'], \n",
+    "                                                               params['soma_params']['tau_m'], \n",
+    "                                                               params['soma_params']['tau_syn1'], \n",
+    "                                                               params['R_m_soma'])\n",
+    "    \n",
+    "    syn_dict_ff = {'receptor_type': 1, 'weight': params['syn_dict_ex']['weight'], 'delay': params['syn_dict_ex']['delay']}\n",
+    "    nest.Connect(spike_generator_1, neuron_1, syn_spec=syn_dict_ff)\n",
+    "    nest.Connect(spike_generator_2, neuron_2, syn_spec=syn_dict_ff)\n",
+    "    \n",
+    "    # record voltage neuron 1, neuron 2\n",
+    "    dap_mm_1 = nest.Create('multimeter', {\"record_from\": [\"dAP_trace\"]})\n",
+    "    nest.Connect(dap_mm_1, neuron_1)\n",
+    "    \n",
+    "    dap_mm_2 = nest.Create('multimeter', {\"record_from\": [\"dAP_trace\"]})\n",
+    "    nest.Connect(dap_mm_2, neuron_2)\n",
+    "    \n",
+    "    vm_1 = nest.Create('voltmeter')\n",
+    "    vm_2 = nest.Create('voltmeter')\n",
+    "    nest.Connect(vm_1, neuron_1)\n",
+    "    nest.Connect(vm_2, neuron_2)\n",
+    "    \n",
+    "    sd_1 = nest.Create('spike_recorder')\n",
+    "    nest.Connect(neuron_1, sd_1)\n",
+    "    \n",
+    "    sd_2 = nest.Create('spike_recorder')\n",
+    "    nest.Connect(neuron_2, sd_2)\n",
+    "    \n",
+    "    synColl = nest.GetConnections(synapse_model=synapse_model_name)\n",
+    "    assert len(synColl) == 1\n",
+    "    \n",
+    "    weights_cs = []\n",
+    "    permanences_cs = []\n",
+    "    weights = []\n",
+    "    permanences = []\n",
+    "    last_sim_time = 0\n",
+    "\n",
+    "    spike_generator_1.origin = nest.GetKernelStatus('biological_time')\n",
+    "    spike_generator_2.origin = nest.GetKernelStatus('biological_time')\n",
+    "\n",
+    "    # connect two neurons\n",
+    "    synColl.set({'permanence': 1.}) \n",
+    "\n",
+    "    for i in range(training_steps):\n",
+    "\n",
+    "        # change toward using the weight recorder, example:\n",
+    "        #wr = nest.Create('weight_recorder')\n",
+    "        #nest.CopyModel('stdp_synapse', 'stdp_synapse_rec', {'weight_recorder': wr})\n",
+    "        nest.SetStatus(neuron_1, {'dAP_trace': z, 'evolve_dAP_trace': 0.})\n",
+    "        nest.SetStatus(neuron_2, {'dAP_trace': z, 'evolve_dAP_trace': 0.})\n",
+    "\n",
+    "        # simulate the network\n",
+    "        sim_time = times_neuron_1[i] - last_sim_time \n",
+    "        nest.Simulate(sim_time)\n",
+    "        last_sim_time = times_neuron_1[i]\n",
+    "\n",
+    "        w_after = synColl.weight\n",
+    "        p_after = synColl.permanence\n",
+    "        weights.append(w_after)\n",
+    "        permanences.append(p_after)\n",
+    "\n",
+    "\n",
+    "    fig, ax = plt.subplots(figsize=(12, 6), nrows=4)\n",
+    "    ax[0].plot(nest.GetStatus(vm_1)[0]['events'][\"times\"], nest.GetStatus(vm_1)[0]['events'][\"V_m\"], label=\"vm1\")\n",
+    "    max_V_m = np.amax(nest.GetStatus(vm_1)[0]['events'][\"V_m\"])\n",
+    "    ax[0].scatter(nest.GetStatus(sd_1)[0]['events']['times'], max_V_m * np.ones_like(nest.GetStatus(sd_1)[0]['events']['times']))\n",
+    "    ax[0].plot(nest.GetStatus(vm_2)[0]['events'][\"times\"], nest.GetStatus(vm_2)[0]['events'][\"V_m\"], label=\"vm2\")\n",
+    "    ax[0].scatter(nest.GetStatus(sd_2)[0]['events']['times'], max_V_m * np.ones_like(nest.GetStatus(sd_2)[0]['events']['times']))\n",
+    "    ax[0].set_ylabel(\"V_m\")\n",
+    "    ax[0].legend()\n",
+    "    \n",
+    "    ax[1].plot(nest.GetStatus(dap_mm_1)[0]['events'][\"times\"], nest.GetStatus(dap_mm_1)[0]['events'][\"dAP_trace\"], label=\"pre\")\n",
+    "    ax[1].plot(nest.GetStatus(dap_mm_2)[0]['events'][\"times\"], nest.GetStatus(dap_mm_2)[0]['events'][\"dAP_trace\"], label=\"post\")\n",
+    "    ax[1].set_ylabel(\"dAP\")\n",
+    "    ax[1].legend()\n",
+    "    \n",
+    "    ax[2].plot(weights)\n",
+    "    ax[2].set_ylabel(\"Weight\")\n",
+    "    \n",
+    "    ax[3].plot(permanences)\n",
+    "    ax[3].set_ylabel(\"Permanence\")\n",
+    "    \n",
+    "    ax[-1].set_xlabel(\"Time [ms]\")\n",
+    "\n",
+    "    fig.suptitle(\"For z = \" + str(z))\n",
+    "    \n",
+    "    fig.savefig(\"/tmp/simulate_stdsp_dependence_on_third_factor_[z=\" + str(z) + \"].png\", dpi=300)\n",
+    "    return weights, permanences\n",
+    "\n",
+    "data = {\"weights_cs\": [], \"permanences_cs\": []}\n",
+    "\n",
+    "zs = [0.,1.,2.]\n",
+    "\n",
+    "for z in zs:\n",
+    "    weights_cs, permanences_cs = simulate_stdsp_dependence_on_third_factor(params, z)\n",
+    "\n",
+    "    data[\"weights_cs\"].append(weights_cs)\n",
+    "    data[\"permanences_cs\"].append(permanences_cs)\n",
+    "    \n",
+    "data[\"zs\"] = zs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---------------------------------\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 960x360 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_stdsp_dependence_on_third_factor(data, params):\n",
+    "    ms = 0.5\n",
+    "    alpha = 0.5\n",
+    "    lw_hline = 1.\n",
+    "    \n",
+    "    #################\n",
+    "    # visualize data\n",
+    "    # ---------------\n",
+    "    gs = mpl.gridspec.GridSpec(1, 3, right=0.92, left=0.09, bottom=0.2, top=0.89, wspace=0.2, hspace=0.2)\n",
+    "    \n",
+    "    # data for Ic=0\n",
+    "    # -------------\n",
+    "    ax1 = plt.subplot(gs[0,0])\n",
+    "    \n",
+    "    training_steps = len(data[\"weights_cs\"][0])\n",
+    "    num_pulses = np.arange(training_steps)\n",
+    "    lns1 = ax1.plot(num_pulses, data[\"weights_cs\"][0], '-o', ms=ms, color='black', label=r'$J$')\n",
+    "    \n",
+    "    #plt.ylabel('weight ($\\mu$S)')\n",
+    "    ax1.set_xlim(0, training_steps)\n",
+    "    ax1.set_ylim(-1, params[\"syn_dict_ee\"]['Wmax']+10)\n",
+    "    #ax1.set_title(r'dAP rate $\\nu_\\mathsf{d}$=%0.1f' % zs[0])\n",
+    "    ax1.set_title(r'$z$=%0.1f' % data['zs'][0])\n",
+    "    ax1.set_ylabel(r'weight $J$ (pA)')\n",
+    "    \n",
+    "    ax2 = ax1.twinx()\n",
+    "    lns2 = ax2.plot(num_pulses, data[\"permanences_cs\"][0], '-o', ms=ms, color='grey', alpha=alpha, label=r'$P$')\n",
+    "    if 'permanence_threshold' in params['syn_dict_ee'].keys():\n",
+    "        plt.hlines(params['syn_dict_ee']['permanence_threshold'], 0, training_steps, lw=lw_hline, color='grey', linestyles='dotted')\n",
+    "    if 'th_perm' in params['syn_dict_ee'].keys():\n",
+    "        plt.hlines(params['syn_dict_ee']['th_perm'], 0, training_steps, lw=lw_hline, color='grey', linestyles='dotted')\n",
+    "\n",
+    "    if \"permanence_max\" in params['syn_dict_ee'].keys():\n",
+    "        plt.hlines(params['syn_dict_ee']['permanence_max'], 0, training_steps, lw=lw_hline, color='grey', linestyles='dashed')\n",
+    "        ax2.set_ylim(-1, params[\"syn_dict_ee\"]['permanence_max']+2)\n",
+    "\n",
+    "    if \"Pmax\" in params['syn_dict_ee'].keys():\n",
+    "        plt.hlines(params['syn_dict_ee']['Pmax'], 0, training_steps, lw=lw_hline, color='grey', linestyles='dashed')    \n",
+    "        ax2.set_ylim(-1, params[\"syn_dict_ee\"]['Pmax']+2)\n",
+    "\n",
+    "    \n",
+    "    ax2.tick_params(axis='y', labelcolor='grey')\n",
+    "    #ax2.set_yticklabels([])\n",
+    "    ax2.set_yticks([])\n",
+    "    ax2.spines['right'].set_color('grey')\n",
+    "    \n",
+    "    # add legends\n",
+    "    lns = [lns1[0],lns2[0]]\n",
+    "    labs = [l.get_label() for l in lns]\n",
+    "    ax1.legend(lns, labs, loc='lower right')\n",
+    "    \n",
+    "    # data for Ic=1\n",
+    "    # -------------\n",
+    "    ax1 = plt.subplot(gs[0,1])\n",
+    "    \n",
+    "    ax1.plot(num_pulses, data[\"weights_cs\"][1], '-o', ms=ms, color='black', label='weight')\n",
+    "    \n",
+    "    ax1.set_ylim(-1, params[\"syn_dict_ee\"]['Wmax']+10)\n",
+    "    ax1.set_xlim(0, training_steps)\n",
+    "    #ax1.set_title(r'dAP rate $\\nu_\\mathsf{d}$=%0.1f' % params['zs'][1])\n",
+    "    ax1.set_title(r'$z$=%0.1f' % data['zs'][1])\n",
+    "    ax1.set_xlabel('number of presynaptic-postsynaptic spike pairings')\n",
+    "    ax1.set_yticks([])\n",
+    "    \n",
+    "    ax2 = ax1.twinx()\n",
+    "    ax2.plot(num_pulses, data[\"permanences_cs\"][1], '-o', ms=ms, color='grey', alpha=alpha, label='permanence')\n",
+    "    if 'permanence_threshold' in params['syn_dict_ee'].keys():\n",
+    "        plt.hlines(params['syn_dict_ee']['permanence_threshold'], 0, training_steps, lw=lw_hline, color='grey', linestyles='dotted')\n",
+    "    if 'th_perm' in params['syn_dict_ee'].keys():\n",
+    "        plt.hlines(params['syn_dict_ee']['th_perm'], 0, training_steps, lw=lw_hline, color='grey', linestyles='dotted')\n",
+    "\n",
+    "    if \"permanence_max\" in params['syn_dict_ee'].keys():\n",
+    "        plt.hlines(params['syn_dict_ee']['permanence_max'], 0, training_steps, lw=lw_hline, color='grey', linestyles='dashed')\n",
+    "        ax2.set_ylim(-1, params[\"syn_dict_ee\"]['permanence_max']+2)\n",
+    "\n",
+    "    if \"Pmax\" in params['syn_dict_ee'].keys():\n",
+    "        plt.hlines(params['syn_dict_ee']['Pmax'], 0, training_steps, lw=lw_hline, color='grey', linestyles='dashed')    \n",
+    "        ax2.set_ylim(-1, params[\"syn_dict_ee\"]['Pmax']+2)\n",
+    "    \n",
+    "    \n",
+    "    ax2.tick_params(axis='y', labelcolor='grey')\n",
+    "    ax2.set_yticks([])\n",
+    "    ax2.spines['right'].set_color('grey')\n",
+    "    \n",
+    "    # data for Ic=2\n",
+    "    # -------------\n",
+    "    ax1 = plt.subplot(gs[0,2])\n",
+    "    \n",
+    "    ax1.plot(num_pulses, data[\"weights_cs\"][2], '-o', ms=ms, color='black', label='weight')\n",
+    "    \n",
+    "    ax1.set_ylim(-1, params[\"syn_dict_ee\"]['Wmax']+10)\n",
+    "    ax1.set_xlim(0, training_steps)\n",
+    "    #ax1.set_title(r'dAP rate $\\nu_\\mathsf{d}$=%0.1f' % params['zs'][2])\n",
+    "    ax1.set_title(r'$z$=%0.1f' % data['zs'][2])\n",
+    "    ax1.set_yticks([])\n",
+    "    \n",
+    "    ax2 = ax1.twinx()\n",
+    "    ax2.plot(num_pulses, data[\"permanences_cs\"][2], '-o', ms=ms, color='grey', alpha=alpha, label=r'$P$')\n",
+    "    if 'permanence_threshold' in params['syn_dict_ee'].keys():\n",
+    "        plt.hlines(params['syn_dict_ee']['permanence_threshold'], 0, training_steps, lw=lw_hline, color='grey', linestyles='dotted')\n",
+    "    if 'th_perm' in params['syn_dict_ee'].keys():\n",
+    "        plt.hlines(params['syn_dict_ee']['th_perm'], 0, training_steps, lw=lw_hline, color='grey', linestyles='dotted')\n",
+    "    if \"permanence_max\" in params['syn_dict_ee'].keys():\n",
+    "        plt.hlines(params['syn_dict_ee']['permanence_max'], 0, training_steps, lw=lw_hline, color='grey', linestyles='dashed')\n",
+    "        ax2.set_ylim(-1, params[\"syn_dict_ee\"]['permanence_max']+2)\n",
+    "\n",
+    "    if \"Pmax\" in params['syn_dict_ee'].keys():\n",
+    "        plt.hlines(params['syn_dict_ee']['Pmax'], 0, training_steps, lw=lw_hline, color='grey', linestyles='dashed')    \n",
+    "        ax2.set_ylim(-1, params[\"syn_dict_ee\"]['Pmax']+2)\n",
+    "\n",
+    "    ax2.tick_params(axis='y', labelcolor='grey')\n",
+    "    ax2.set_ylabel(r\"permanence $P$\", color=\"grey\")\n",
+    "    #ax2.spines['right'].set_color('grey')\n",
+    "    \n",
+    "    print('---------------------------------')\n",
+    "    path = '.'\n",
+    "    fname = 'plasticity_dynamics'\n",
+    "    plt.savefig(\"/tmp/%s.png\" % fname)\n",
+    "\n",
+    "plot_stdsp_dependence_on_third_factor(data, params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plotted is the evolution of the synaptic permanence (gray) and weight (black) during repetitive presynaptic-postsynaptic spike pairing for different levels of the dAP activity. In the depicted example, presynaptic spikes precede the postsynaptic spikes by 40 ms for each spike pairing. Consecutive spike pairs are separated by a 200 ms interval. In each panel, the postsynaptic dAP trace is clamped at a different value: $z = 0$ (left), $z = 1$ (middle), and $z = 2$ (right). The dAP target activity is fixed at $z^\\ast = 1$. The horizontal dashed and dotted lines mark the maximum permanence $P_\\text{max}$ and the maturity threshold $\\theta_P$, respectively."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Experiment 3: Network sequence training and replay\n",
+    "\n",
+    "Now that we have characterised the response of individual neurons and synapses, we take the next step and create the sequence learning network for a dictionary of 6 items (represented here as A, B, C, D, E and F)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def generate_sequences(params, fname):\n",
+    "    \"\"\"Generate sequence of elements using three methods:\n",
+    "    1. randomly drawn elements from a vocabulary\n",
+    "    2. sequences with transition matrix\n",
+    "    3. higher order sequences: sequences with shared subsequences\n",
+    "    4. hard coded sequences\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    params : dict\n",
+    "        dictionary contains task parameters\n",
+    "    fname       : str\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    sequences: list\n",
+    "    test_sequences: list\n",
+    "    vocabulary: list\n",
+    "    \"\"\"\n",
+    "\n",
+    "    task_name = params['task_name']\n",
+    " \n",
+    "    # set of characters used to build the sequences\n",
+    "    vocabulary = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T',\n",
+    "                  'U', 'V', 'W', 'X', 'Y', 'Z'][:params['vocab_size']]\n",
+    "    sequences = []\n",
+    "\n",
+    "    # create high order sequences, characters are drawn without replacement\n",
+    "    if task_name == \"high_order\":\n",
+    "\n",
+    "        if (params['num_sequences'] % params['num_sub_seq'] != 0):\n",
+    "            raise ZeroDivisionError(\n",
+    "                'for high order sequences number of sequences needs (\"num_sequences\") to be divisible by num_sub_seq')\n",
+    "\n",
+    "        num_sequences_high_order = int(params['num_sequences'] / params['num_sub_seq'])\n",
+    "        for i in range(num_sequences_high_order):\n",
+    "            characters_sub_seq = copy.copy(vocabulary)\n",
+    "            sub_seq = random.sample(characters_sub_seq, params[\"length_sequence\"] - 2)\n",
+    "            for char in sub_seq:\n",
+    "                characters_sub_seq.remove(char)\n",
+    "\n",
+    "            for j in range(params['num_sub_seq']):\n",
+    "                # remove the characters that were chosen for the end and the start of the sequence\n",
+    "                # this is to avoid sequences with adjacent letters of the same kind\n",
+    "                # we will add this feature to the code asap \n",
+    "                end_char = random.sample(characters_sub_seq, 1)\n",
+    "                characters_sub_seq.remove(end_char[0])\n",
+    "\n",
+    "                start_char = random.sample(characters_sub_seq, 1)\n",
+    "                characters_sub_seq.remove(start_char[0])\n",
+    "\n",
+    "                sequence = start_char + sub_seq + end_char\n",
+    "                sequences.append(sequence)\n",
+    "\n",
+    "    # randomly shuffled characters\n",
+    "    elif task_name == \"random\":\n",
+    "        # pick unique initial characters\n",
+    "        initial_characters = vocabulary.copy()\n",
+    "        np.random.shuffle(initial_characters)\n",
+    "        initial_characters = initial_characters[:params['num_sequences']]\n",
+    "\n",
+    "        sequences = params['num_sequences'] * [None]\n",
+    "        for i in range(len(sequences)):\n",
+    "            # sequences[i] = params[\"length_sequence\"] * [initial_characters[i]]\n",
+    "            # while repeated_characters(sequences[i]):\n",
+    "            #vocabulary_minus_initial = list(set(vocabulary) - set(initial_characters))\n",
+    "            sequences[i] = [initial_characters[i]] + list(np.random.choice(vocabulary, params[\"length_sequence\"] - 1))\n",
+    "            \n",
+    "        # sequences = [np.random.choice(vocabulary, params[\"length_sequence\"]) for _ in range(params['num_sequences'])]\n",
+    "\n",
+    "    # create sequences using matrix transition \n",
+    "    elif task_name == \"structure\":\n",
+    "        matrix_transition = defaultdict(list)\n",
+    "        for char in vocabulary:\n",
+    "            x = np.random.choice(2, len(vocabulary), p=[0.2, 0.8])\n",
+    "            matrix_transition[char] = x / sum(x)\n",
+    "\n",
+    "        for _ in range(params['num_sequences']):\n",
+    "            sequence = random.sample(vocabulary, 1)\n",
+    "            last_char = sequence[-1]\n",
+    "            for _ in range(params[\"length_sequence\"] - 1):\n",
+    "                sequence += np.random.choice(vocabulary, 1, p=matrix_transition[last_char])[0]\n",
+    "                last_char = sequence[-1]\n",
+    "\n",
+    "            sequences += [sequence]\n",
+    "    else:\n",
+    "        assert task_name == \"hard_coded\"\n",
+    "        # hard coded sequences \n",
+    "        task_type = params['task_type']\n",
+    "        if task_type == 1:\n",
+    "            sequences = [['A', 'D', 'B', 'E'], ['F', 'D', 'B', 'C']]\n",
+    "        elif task_type == 2:\n",
+    "            sequences = [['E', 'N', 'D', 'I', 'J'], ['L', 'N', 'D', 'I', 'K'], ['G', 'J', 'M', 'C', 'N'], \n",
+    "                         ['F', 'J', 'M', 'C', 'I'], ['B', 'C', 'K', 'H', 'I'], ['A', 'C', 'K', 'H', 'F']]\n",
+    "        elif task_type == 3:\n",
+    "            sequences = [['E', 'N', 'D', 'I', 'J'], ['L', 'N', 'D', 'I', 'K'], ['G', 'J', 'M', 'E', 'N'], \n",
+    "                         ['F', 'J', 'M', 'E', 'I'], ['B', 'C', 'K', 'B', 'I'], ['A', 'C', 'K', 'B', 'F']]\n",
+    "        elif task_type == 6:\n",
+    "            sequences = [['A', 'D', 'B', 'E'], ['F', 'D', 'B', 'C'], ['C', 'D', 'B', 'G']]\n",
+    "        else:\n",
+    "            #sequences = [['A', 'D', 'B', 'G', 'H', 'E'], ['F', 'D', 'B', 'G', 'H', 'C'], ['C', 'D', 'H', 'D', 'B', 'G']]\n",
+    "            sequences = [['A', 'D', 'B', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N','E'], ['F', 'D', 'B', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'C']]\n",
+    "\n",
+    "    # test sequences used to measure the accuracy \n",
+    "    test_sequences = sequences\n",
+    "\n",
+    "    if params['store_training_data']:\n",
+    "        fname = 'training_data'\n",
+    "        fname_voc = 'vocabulary'\n",
+    "        print(\"\\nSave training data to %s\" % ( fname))\n",
+    "        np.save('%s' % ( fname), sequences)\n",
+    "        np.save('%s' % ( fname_voc), vocabulary)\n",
+    "\n",
+    "    return sequences, test_sequences, vocabulary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def derived_parameters(params):\n",
+    "    \"\"\"Set additional parameters derived from base parameters.\n",
+    "\n",
+    "    A dictionary containing all (base and derived) parameters is stored as model attribute params\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    params:    dict\n",
+    "               Parameter dictionary\n",
+    "    \"\"\"\n",
+    "\n",
+    "    params = copy.deepcopy(params)\n",
+    "\n",
+    "    synapse_model_name = params['syn_dict_ee']['synapse_model']\n",
+    "\n",
+    "    # connection rules for EE connections\n",
+    "    params['conn_dict_ee'] = {}\n",
+    "    params['conn_dict_ee']['rule'] = params['rule']\n",
+    "    params['conn_dict_ee']['indegree'] = int(params['connection_prob'] *\n",
+    "                                                  params['M'] *\n",
+    "                                                  params['n_E'])\n",
+    "    params['conn_dict_ee']['allow_autapses'] = False\n",
+    "    params['conn_dict_ee']['allow_multapses'] = False\n",
+    "\n",
+    "    # compute neuron's membrane resistance\n",
+    "    params['R_m_soma'] = params['soma_params']['tau_m'] / params['soma_params']['C_m']\n",
+    "    params['R_m_inhibit'] = params['inhibit_params']['tau_m'] / params['inhibit_params']['C_m']\n",
+    "\n",
+    "    # compute psc max from the psp max\n",
+    "    params['J_IE_psp'] = 1.2 * params['inhibit_params']['V_th']         # inhibitory PSP as a response to an input from E neuron\n",
+    "\n",
+    "    if params['evaluate_replay']:\n",
+    "        params['J_IE_psp'] /= params['n_E']\n",
+    "    else:\n",
+    "        params['J_IE_psp'] /= params['pattern_size']        \n",
+    "\n",
+    "    params['syn_dict_ex']['weight'] = psp_max_2_psc_max(params['J_EX_psp'], params['soma_params']['tau_m'],\n",
+    "                                                   params['soma_params']['tau_syn1'], params['R_m_soma'])\n",
+    "    params['syn_dict_ie']['weight'] = psp_max_2_psc_max(params['J_IE_psp'], params['inhibit_params']['tau_m'],\n",
+    "                                                   params['inhibit_params']['tau_syn_ex'],\n",
+    "                                                   params['R_m_inhibit'])\n",
+    "    params['syn_dict_ei']['weight'] = psp_max_2_psc_max(params['J_EI_psp'], params['soma_params']['tau_m'],\n",
+    "                                                   params['soma_params']['tau_syn3'], params['R_m_soma'])\n",
+    "\n",
+    "    # set initial weights (or permanences in the case of the structural synapse)\n",
+    "    import nest\n",
+    "    params['syn_dict_ee']['permanence'] = nest.random.uniform(min=params['permanence_min'], max=params['permanence_max']) \n",
+    "\n",
+    "    if \"synapse_nestml\" in synapse_model_name:\n",
+    "        params['syn_dict_ee']['dt_max'] = 2.*params['DeltaT']              # maximum time lag for the STDP window \n",
+    "    else:\n",
+    "        params['syn_dict_ee']['dt_max'] = -2.*params['DeltaT']              # maximum time lag for the STDP window \n",
+    "    params['DeltaT_seq'] = 2.5*params['DeltaT']                         # inter-sequence interval\n",
+    "    \n",
+    "    # clamp DeltaT_seq if it exceeds the duration of the dAP\n",
+    "    if params['DeltaT_seq'] < params['soma_params']['tau_dAP']:\n",
+    "        params['DeltaT_seq'] = params['soma_params']['tau_dAP']\n",
+    "     \n",
+    "    print('\\n#### postsynaptic potential ####')\n",
+    "    print('PSP maximum J_EX psp:  %f mV' % params['J_EX_psp'])\n",
+    "    print('PSP maximum J_IE psp:  %f mV' % params['J_IE_psp'])\n",
+    "    print('PSP maximum J_EI psp:  %f mV' % params['J_EI_psp'])\n",
+    "\n",
+    "    print('\\n#### postsynaptic current ####')\n",
+    "    print('PSC maximum J_EX:  %f pA' % params['syn_dict_ex']['weight'])\n",
+    "    print('PSC maximum J_IE:  %f pA' % params['syn_dict_ie']['weight'])\n",
+    "    print('PSC maximum J_EI:  %f pA' % params['syn_dict_ei']['weight'])\n",
+    "\n",
+    "    return params\n",
+    "\n",
+    "\n",
+    "###############################################################################\n",
+    "def parameter_set_list(P):\n",
+    "    \"\"\" Generate list of parameters sets\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    P : dict  \n",
+    "        parameter space \n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    l : list \n",
+    "        list of parameter sets \n",
+    "    \"\"\"\n",
+    "\n",
+    "    l = []\n",
+    "    for z in P.iter_inner():\n",
+    "        p = copy.deepcopy(dict(z))\n",
+    "        l.append(p)\n",
+    "        l[-1]['label'] = hashlib.md5(pformat(l[-1]).encode(\n",
+    "            'utf-8')).hexdigest()  ## add md5 checksum as label of parameter set (used e.g. for data file names) \n",
+    "\n",
+    "    return l\n",
+    "\n",
+    "\n",
+    "\n",
+    "##############################################\n",
+    "def load_spike_data(label, skip_rows=3):\n",
+    "    \"\"\"Load spike data from files.\n",
+    "\n",
+    "    Parameters\n",
+    "    ---------\n",
+    "    label:          str\n",
+    "                    Spike file label (file name root).\n",
+    "\n",
+    "    skip_rows:      int, optional\n",
+    "                    Number of rows to be skipped while reading spike files (to remove file headers). The default is 3.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    spikes:   numpy.ndarray\n",
+    "              Lx2 array of spike senders spikes[:,0] and spike times spikes[:,1] (L = number of spikes).\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # get list of files names\n",
+    "    files = []\n",
+    "    path=\".\"\n",
+    "    for file_name in os.listdir(path):\n",
+    "        if file_name.endswith('.dat') and file_name.startswith(label):\n",
+    "            files += [file_name]\n",
+    "    files.sort()\n",
+    "    #files = [label + \".dat\"]\n",
+    "\n",
+    "    assert len(files) > 0, \"No files of type '%s*.dat' found.\" % (label)\n",
+    "\n",
+    "    # open spike files and read data\n",
+    "    spikes = []\n",
+    "    for file_name in files:\n",
+    "        try:\n",
+    "            spikes += [np.loadtxt('%s' % (file_name),skiprows=skip_rows)] ## load spike file while skipping the header \n",
+    "        except:\n",
+    "            print('Error: %s' % sys.exc_info()[1])\n",
+    "            print('Remove non-numeric entries from file %s (e.g. in file header) by specifying (optional) parameter \"skip_rows\".\\n' % (file_name))\n",
+    "    \n",
+    "    try:\n",
+    "        spikes = np.concatenate([spike for spike in spikes if spike.size>0])\n",
+    "    except:\n",
+    "        print(\"All files are empty\")\n",
+    "\n",
+    "#    # open spike files and read data\n",
+    "#    spikes = []\n",
+    "#    for file_name in files:\n",
+    "#        try:\n",
+    "#            spikes += [np.loadtxt('%s/%s' % (path, file_name),\n",
+    "#                                  skiprows=skip_rows)]  ## load spike file while skipping the header\n",
+    "#            print(spikes)\n",
+    "#        except:\n",
+    "#            print(\"Error: %s\" % sys.exc_info()[1])\n",
+    "#            print(\n",
+    "#                \"Remove non-numeric entries from file %s (e.g. in file header) by specifying (optional) parameter 'skip_rows'.\\n\" % (\n",
+    "#                    file_name))\n",
+    "#\n",
+    "#    spikes = np.concatenate(spikes)\n",
+    "#\n",
+    "    return spikes\n",
+    "\n",
+    "\n",
+    "###############################################################################\n",
+    "def load_data(fname):\n",
+    "    \"\"\"Load data\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    path: str\n",
+    "    fname: str\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    data: ndarray\n",
+    "    \"\"\"\n",
+    "\n",
+    "    #TODO: this is temporary hack!\n",
+    "    try:\n",
+    "      data = np.load('%s.npy' % fname, allow_pickle=True).item()\n",
+    "    except:\n",
+    "      data = np.load('%s.npy' % fname, allow_pickle=True)\n",
+    "\n",
+    "    return data\n",
+    "\n",
+    "\n",
+    "###############################################################################\n",
+    "def number_active_neurons_per_element(test_sequences, times_somatic_spikes, senders_somatic_spikes, excitation_times,\n",
+    "                                      fixed_somatic_delay):\n",
+    "    \"\"\"\n",
+    "    Finds the active neurons of each element in the sequences and return their number\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    test_sequences         : list\n",
+    "    times_somatic_spikes   : ndarray\n",
+    "    senders_somatic_spikes : ndarray\n",
+    "    excitation_times       : list\n",
+    "    fixed_somatic_delay    : float\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    num_active_neurons_per_sequence : list\n",
+    "    \"\"\"\n",
+    "\n",
+    "    num_active_neurons_per_sequence = []\n",
+    "    end_iterations = 0\n",
+    "\n",
+    "    assert len(excitation_times) >= 2, \"excitation times need to contain at leasts 2 components\"\n",
+    "    DeltaT = excitation_times[1] - excitation_times[0]\n",
+    "\n",
+    "    # for each sequence in the test sequences\n",
+    "    for seq in test_sequences:\n",
+    "        start_iterations = end_iterations\n",
+    "        end_iterations += len(seq)\n",
+    "        num_active_neurons = {}\n",
+    "\n",
+    "        # for each character in the sequence\n",
+    "        for k, (j, char) in enumerate(zip(range(start_iterations, end_iterations), seq)):\n",
+    "            indices_soma = np.where((times_somatic_spikes < excitation_times[j] + DeltaT) & \n",
+    "                                    (times_somatic_spikes > excitation_times[j]))\n",
+    "            senders_soma = senders_somatic_spikes[indices_soma]\n",
+    "\n",
+    "            num_active_neurons[char] = len(senders_soma)\n",
+    "\n",
+    "        num_active_neurons_per_sequence.append(num_active_neurons)\n",
+    "\n",
+    "    return num_active_neurons_per_sequence\n",
+    "\n",
+    "\n",
+    "###############################################################################\n",
+    "def measure_sequences_overlap(test_sequences, times_somatic_spikes, senders_somatic_spikes, excitation_times,\n",
+    "                              fixed_somatic_delay, number_training_episodes):\n",
+    "    \"\"\"Finds the shared active neurons between the last sequence elements\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    test_sequences         : list\n",
+    "    times_somatic_spikes   : ndarray\n",
+    "    senders_somatic_spikes : ndarray\n",
+    "    excitation_times       : list\n",
+    "    fixed_somatic_delay    : float\n",
+    "    number_training_episodes : int\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    episodes_overlap       : list\n",
+    "    \"\"\"\n",
+    "\n",
+    "    sequences_active_neurons = [[] for _ in range(len(test_sequences))]\n",
+    "    end_iterations = 0\n",
+    "    episodes_overlap = []\n",
+    "\n",
+    "    for training_episodes in range(number_training_episodes):\n",
+    "        # for each sequence in the test sequences\n",
+    "        for i, seq in enumerate(test_sequences):\n",
+    "            start_iterations = end_iterations\n",
+    "            end_iterations += len(seq)\n",
+    "            active_neurons = []\n",
+    "\n",
+    "            # for each character in the sequence\n",
+    "            for k, (j, char) in enumerate(zip(range(start_iterations, end_iterations), seq)):\n",
+    "                indices_soma = np.where((times_somatic_spikes < excitation_times[j] + fixed_somatic_delay) & (\n",
+    "                        times_somatic_spikes > excitation_times[j]))\n",
+    "                senders_soma = senders_somatic_spikes[indices_soma]\n",
+    "\n",
+    "                active_neurons.append(senders_soma)\n",
+    "\n",
+    "            sequences_active_neurons[i] = active_neurons\n",
+    "\n",
+    "        # compute overlap \n",
+    "        co = 0\n",
+    "        sequences_overlap = []\n",
+    "        # TODO: use variable for test_sequences[0]\n",
+    "        for q in range(len(test_sequences[0])):\n",
+    "            overlap = [value for value in sequences_active_neurons[co][q] if\n",
+    "                       value in sequences_active_neurons[co + 1][q]]\n",
+    "            size_overlap = len(overlap)\n",
+    "            sequences_overlap.append(size_overlap)\n",
+    "        # TODO here the overlap is computed only between two sequences\n",
+    "        co += 2\n",
+    "\n",
+    "        episodes_overlap.append(sequences_overlap)\n",
+    "\n",
+    "    return episodes_overlap\n",
+    "\n",
+    "\n",
+    "###############################################################################\n",
+    "def compute_prediction_performance(somatic_spikes, dendriticAP, dendriticAP_recording_times,\n",
+    "                                   characters_to_subpopulations, test_seq, params):\n",
+    "    \"\"\"Computes prediction performance including: error, false positive and false negative\n",
+    "    The prediction error is computed as the Euclidean distance between the target vector and the output vector for each last character `q` in a sequence.\n",
+    "    The output vector `o` is an M dimensional binary vector, where oi = 1 if the ith subpopulation is predicted, and oi= 0 else.\n",
+    "    A subpopulation is considered predicted if it contains at least `ratio_fp_activation*n_E` neurons with a dAP.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    somatic_spikes   : ndarray\n",
+    "        Lx2 array of spike senders somatic_spikes[:,0] and spike times somatic_spikes[:,1]\n",
+    "                       (L = number of spikes).\n",
+    "    dendriticAP      : ndarray\n",
+    "        Lx3 array of current senders dendriticAP[:,0], current times dendriticAP[:,1],\n",
+    "        and current dendriticAP[:,2] (L = number of recorded data points).\n",
+    "    dendriticAP_recording_times  : list\n",
+    "        list of list containing times at which the dendritic current is recorded for a given \n",
+    "        element in each sequence\n",
+    "    characters_to_subpopulations : dict\n",
+    "    test_seq  : list\n",
+    "        list of list containing sequence elements\n",
+    "    params    : dict\n",
+    "        parameter dictionary\n",
+    "    \"\"\"\n",
+    "\n",
+    "    errors = [[] for _ in range(len(test_seq))]\n",
+    "    false_positives = [[] for _ in range(len(test_seq))]\n",
+    "    false_negatives = [[] for _ in range(len(test_seq))]\n",
+    "    last_char_active_neurons = [[] for _ in range(len(test_seq))]\n",
+    "    last_char_active_dendrites = [[] for _ in range(len(test_seq))]\n",
+    "\n",
+    "    seqs = copy.copy(test_seq)\n",
+    "\n",
+    "    for seq_num, seq in enumerate(test_seq):\n",
+    "        recording_times = dendriticAP_recording_times[seq_num]\n",
+    "\n",
+    "        for it, rc_time in enumerate(recording_times):\n",
+    "\n",
+    "            # find dendritic action potentials (dAPs)\n",
+    "            idx_q = np.where((dendriticAP[:, 1] < rc_time + params['idend_record_time'] + 1.) & \n",
+    "                               (dendriticAP[:, 1] > rc_time))[0]\n",
+    "\n",
+    "            idx_dAP = np.where(dendriticAP[:, 2][idx_q] > params['soma_params']['I_p'] - 1.)[0]\n",
+    "            \n",
+    "            senders_dAP = dendriticAP[:, 0][idx_q][idx_dAP]\n",
+    " \n",
+    "            subpopulation_senders_dAP = [int((s - 1) // params['n_E']) for s in senders_dAP]\n",
+    "\n",
+    "            # find somatic action potentials\n",
+    "            idx_soma = np.where((somatic_spikes[:, 1] < rc_time + 2*params['DeltaT']) & \n",
+    "                                (somatic_spikes[:, 1] > rc_time + params['DeltaT']))[0]\n",
+    "            senders_soma = somatic_spikes[:, 0][idx_soma]\n",
+    "            num_active_neurons = len(senders_soma)\n",
+    "            num_active_dendrites = len(senders_dAP)\n",
+    "\n",
+    "            # create the target vector \n",
+    "            excited_subpopulations = characters_to_subpopulations[seqs[seq_num][-1]]\n",
+    "            excited_subpopulations_prev = characters_to_subpopulations[seqs[seq_num][-2]]\n",
+    "            target = np.zeros(params['M'])\n",
+    "            target[excited_subpopulations] = 1\n",
+    "\n",
+    "            # count false positives and construct the output vector\n",
+    "            output = np.zeros(params['M'])\n",
+    "            count_subpopulations = Counter(subpopulation_senders_dAP)\n",
+    "            counter_correct = 0\n",
+    "\n",
+    "            #ratio_fn_activation = 0.8\n",
+    "            #ratio_fp_activation = 0.1\n",
+    "            ratio_fn_activation = 0.5\n",
+    "            ratio_fp_activation = 0.5\n",
+    "\n",
+    "            for k, v in count_subpopulations.items():\n",
+    "                if k not in excited_subpopulations and v >= (ratio_fp_activation * params['pattern_size']):\n",
+    "                    #print('episode %d/%d count of a false positive %d, %d' % (it, len(recording_times), k, v))\n",
+    "                    output[k] = 1\n",
+    "                elif k in excited_subpopulations and v >= (ratio_fn_activation * params['pattern_size']):\n",
+    "                    counter_correct += 1\n",
+    "\n",
+    "            # find false negatives\n",
+    "            if counter_correct == params['L']:\n",
+    "                output[excited_subpopulations] = 1\n",
+    "            #else:\n",
+    "            #    false_negative = 1\n",
+    "\n",
+    "            error = 1/params['L'] * np.sqrt(sum((output - target) ** 2))\n",
+    "            false_positive = 1/params['L'] * sum(np.heaviside(output - target, 0))\n",
+    "            false_negative = 1/params['L'] * sum(np.heaviside(target - output, 0))\n",
+    "\n",
+    "            # append errors, fp, and fn for the different sequences\n",
+    "            errors[seq_num].append(error)\n",
+    "            false_positives[seq_num].append(false_positive)\n",
+    "            false_negatives[seq_num].append(false_negative)\n",
+    "            last_char_active_neurons[seq_num].append(num_active_neurons)\n",
+    "            last_char_active_dendrites[seq_num].append(num_active_dendrites)\n",
+    "\n",
+    "        print('#### Prediction performance ####')\n",
+    "        print('Sequence:', seqs[seq_num])\n",
+    "        print('Error:', errors[seq_num][-1])\n",
+    "        print('False positives:', false_positives[seq_num][-1])\n",
+    "        print('False negatives:', false_negatives[seq_num][-1])\n",
+    "        print('Number of active neurons in %s: %d' % (seqs[seq_num][-1], last_char_active_neurons[seq_num][-1]))\n",
+    "        print('Number of active dendrites in %s: %d' % (seqs[seq_num][-1], last_char_active_dendrites[seq_num][-1]))\n",
+    "\n",
+    "    seq_avg_errors = np.mean(errors, axis=0)\n",
+    "    seq_avg_false_positives = np.mean(false_positives, axis=0)\n",
+    "    seq_avg_false_negatives = np.mean(false_negatives, axis=0)\n",
+    "    seq_avg_last_char_active_neurons = np.mean(last_char_active_neurons, axis=0)\n",
+    "\n",
+    "    return seq_avg_errors, seq_avg_false_positives, seq_avg_false_negatives, seq_avg_last_char_active_neurons\n",
+    "\n",
+    "\n",
+    "###############################################################################\n",
+    "def hebbian_contribution(facilitate_factor, tau_plus, W_max, delta_t=40.):\n",
+    "    \"\"\"Computes the increment of the facilitate function of the additive STDP \n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    facilitate_factor : float\n",
+    "    delta_T           : float\n",
+    "    tau_plus          : float\n",
+    "    W_max             : float\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    increment : float\n",
+    "    \"\"\"\n",
+    "\n",
+    "    increment = facilitate_factor * W_max * np.exp(-delta_t / tau_plus)\n",
+    "    #increment = facilitate_factor * W_max\n",
+    "\n",
+    "    return increment\n",
+    "\n",
+    "\n",
+    "###############################################################################\n",
+    "def homeostasis_contribution(hs, Wmax=1, r_d=0, r_t=1):\n",
+    "    \"\"\" homeostasis plastic change\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    hs   : float\n",
+    "    r_d  : float            \n",
+    "    r_t  : float\n",
+    "    \"\"\"\n",
+    "\n",
+    "    return hs * (r_t - r_d) * Wmax\n",
+    "\n",
+    "\n",
+    "###############################################################################\n",
+    "def synaptic_plastic_change(facilitate_factor, tau_plus, w_max, hs, delta_t=40.):\n",
+    "    \"\"\" compute the plastic change due to Hebbian learning and homeostasis\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    facilitate_factor   : float\n",
+    "    tau_plus            : float\n",
+    "    w_max               : float\n",
+    "    hs                  : float\n",
+    "    delta_t             : float\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    w_tot               : float\n",
+    "    \"\"\"\n",
+    "\n",
+    "    w_inc = hebbian_contribution(facilitate_factor, tau_plus, w_max, delta_t)\n",
+    "    w_hom = homeostasis_contribution(hs, w_max)\n",
+    "\n",
+    "    w_tot = w_inc + w_hom\n",
+    "\n",
+    "    return w_tot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Model:\n",
+    "    \"\"\"Instantiation of the Spiking-TemporalMemory model and its PyNEST implementation.\n",
+    "\n",
+    "    the model provides the following member functions: \n",
+    "\n",
+    "    __init__(parameters)\n",
+    "    create()\n",
+    "    connect()\n",
+    "    simulate(t_sim)\n",
+    "\n",
+    "    In addition, each model may implement other model-specific member functions.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, params, sequences, vocabulary):\n",
+    "        \"\"\"Initialize model and simulation instance, including\n",
+    "\n",
+    "        1) parameter setting,\n",
+    "        2) generate sequence data,\n",
+    "        3) configuration of the NEST kernel,\n",
+    "        4) setting random-number generator seed, and\n",
+    "\n",
+    "        Parameters\n",
+    "        ----------\n",
+    "        params:    dict\n",
+    "                   Parameter dictionary\n",
+    "        \"\"\"\n",
+    "\n",
+    "        print('\\nInitialising model and simulation...')\n",
+    "\n",
+    "        # set parameters derived from base parameters\n",
+    "        self.params = derived_parameters(params)\n",
+    "        print(\"Model parameters: \" + str(self.params))\n",
+    "\n",
+    "        # set network size\n",
+    "        self.num_subpopulations = params['M']\n",
+    "        self.num_exc_neurons = params['n_E'] * self.num_subpopulations\n",
+    "\n",
+    "        # initialize RNG        \n",
+    "        np.random.seed(self.params['seed'])\n",
+    "        random.seed(self.params['seed'])\n",
+    "\n",
+    "        # input stream: sequence data\n",
+    "        self.sequences = sequences\n",
+    "        self.vocabulary = vocabulary\n",
+    "        self.length_sequence = len(self.sequences[0])\n",
+    "        self.num_sequences = len(self.sequences)\n",
+    "\n",
+    "        # initialize the NEST kernel\n",
+    "        self.__setup_nest()\n",
+    "\n",
+    "        # get time constant for dendriticAP rate\n",
+    "        self.params['soma_params']['tau_h'] = self.__get_time_constant_dendritic_rate(\n",
+    "            calibration=self.params['calibration'])\n",
+    "\n",
+    "    def __setup_nest(self):\n",
+    "        \"\"\"Initializes the NEST kernel.\n",
+    "        \"\"\"\n",
+    "\n",
+    "        nest.ResetKernel()\n",
+    "        nest.Install(module_name)\n",
+    "        nest.set_verbosity(nest_verbosity)\n",
+    "        nest.SetKernelStatus({\n",
+    "            'resolution': self.params['dt'],\n",
+    "            'print_time': self.params['print_simulation_progress'],\n",
+    "            'local_num_threads': n_threads,\n",
+    "            'rng_seed': self.params['seed'],\n",
+    "            'dict_miss_is_error': True,\n",
+    "            'overwrite_files': self.params['overwrite_files'],\n",
+    "            'data_prefix': ''\n",
+    "        })\n",
+    "\n",
+    "    def create(self):\n",
+    "        \"\"\"Create and configure all network nodes (neurons + recording and stimulus devices)\n",
+    "        \"\"\"\n",
+    "\n",
+    "        print('\\nCreating and configuring nodes...')\n",
+    "\n",
+    "        # create excitatory population\n",
+    "        self.__create_neuronal_populations()\n",
+    "\n",
+    "        # compute timing of the external inputs and recording devices\n",
+    "        # TODO: this function should probably not be part of the model\n",
+    "        excitation_times, excitation_times_neuron, idend_recording_times = self.__compute_timing_external_inputs(\n",
+    "                self.params['DeltaT'], self.params['DeltaT_seq'], self.params['DeltaT_cue'], \n",
+    "                self.params['excitation_start'], self.params['time_dend_to_somatic'])\n",
+    "\n",
+    "        # create spike generators\n",
+    "        self.__create_spike_generators(excitation_times_neuron)\n",
+    "\n",
+    "        # create recording devices\n",
+    "        self.__create_recording_devices(excitation_times, idend_recording_times)\n",
+    "\n",
+    "        # create weight recorder\n",
+    "        if self.params['active_weight_recorder']:\n",
+    "            self.__create_weight_recorder()\n",
+    "\n",
+    "    def connect(self):\n",
+    "        \"\"\"Connects network and devices\n",
+    "        \"\"\"\n",
+    "\n",
+    "        print('\\nConnecting network and devices...')\n",
+    "        # TODO: take into account L (number of subpopulations per character) when connecting the network\n",
+    "\n",
+    "        # connect excitatory population (EE)\n",
+    "        if self.params['load_connections']:\n",
+    "            print(\"\\tLoading connections from file\")\n",
+    "            self.__load_connections(label='ee_connections')\n",
+    "        else:\n",
+    "            print(\"\\tCreating new random connections\")\n",
+    "            self.__connect_excitatory_neurons()\n",
+    "\n",
+    "            # connect inhibitory population (II, EI, IE)\n",
+    "        self.__connect_inhibitory_neurons()\n",
+    "\n",
+    "        # connect external input\n",
+    "        self.__connect_external_inputs_to_subpopulations()\n",
+    "\n",
+    "        # connect neurons to the spike recorder\n",
+    "        nest.Connect(self.exc_neurons, self.spike_recorder_soma)\n",
+    "        nest.Connect(self.exc_neurons, self.spike_recorder_soma_)\n",
+    "        nest.Connect(self.inh_neurons, self.spike_recorder_inh)\n",
+    "        nest.Connect(self.inh_neurons, self.spike_recorder_inh_)\n",
+    "\n",
+    "        # connect multimeter for recording dendritic current\n",
+    "        if self.params['evaluate_performance']:\n",
+    "            nest.Connect(self.multimeter_idend_eval, self.exc_neurons)\n",
+    "            nest.Connect(self.multimeter_idend_eval_, self.exc_neurons)\n",
+    "            nest.Connect(self.multimeter_vm_eval_, self.exc_neurons)\n",
+    "\n",
+    "        # connect multimeter for recording dendritic current from all subpopulations of the last trial\n",
+    "        if self.params['record_idend_last_episode']:\n",
+    "            nest.Connect(self.multimeter_idend_last_episode, self.exc_neurons)\n",
+    "\n",
+    "        # set min synaptic strength\n",
+    "        self.__set_min_synaptic_strength()\n",
+    "\n",
+    "    def simulate(self):\n",
+    "        \"\"\"Run simulation.\n",
+    "        \"\"\"\n",
+    "\n",
+    "        # the simulation time is set during the creation of the network  \n",
+    "        if nest.Rank() == 0:\n",
+    "            print('\\nSimulating {} ms.'.format(self.sim_time))\n",
+    "\n",
+    "        nest.Simulate(self.sim_time)\n",
+    "\n",
+    "    def __create_neuronal_populations(self):\n",
+    "        \"\"\"'Create neuronal populations\n",
+    "        \"\"\"\n",
+    "\n",
+    "        # create excitatory population\n",
+    "        self.exc_neurons = nest.Create(self.params['soma_model'],\n",
+    "                                       self.num_exc_neurons,\n",
+    "                                       params=self.params['soma_params'])\n",
+    "\n",
+    "        # create inhibitory population\n",
+    "        self.inh_neurons = nest.Create(self.params['inhibit_model'],\n",
+    "                                       self.params['n_I'] * self.num_subpopulations,\n",
+    "                                       params=self.params['inhibit_params'])\n",
+    "\n",
+    "    def __create_spike_generators(self, excitation_times_neuron):\n",
+    "        \"\"\"Create spike generators\n",
+    "        \"\"\"\n",
+    "\n",
+    "        excitation_times_soma, excitation_times_dend = excitation_times_neuron \n",
+    "\n",
+    "        self.input_excitation_soma = {}\n",
+    "        self.input_excitation_dend = {}\n",
+    "        for char in self.vocabulary:\n",
+    "            self.input_excitation_soma[char] = nest.Create('spike_generator')\n",
+    "            self.input_excitation_dend[char] = nest.Create('spike_generator')\n",
+    "\n",
+    "        # set spike generator status with the above computed excitation times\n",
+    "        for char in self.vocabulary:\n",
+    "            nest.SetStatus(self.input_excitation_soma[char], {'spike_times': excitation_times_soma[char]})\n",
+    "            #print(\"For spike generator \" + char + \", spike times = \" + str(excitation_times_soma[char]))\n",
+    "\n",
+    "        # this makes the first population in the sequence sparse\n",
+    "        if self.params['sparse_first_char']:\n",
+    "            first_chars = [char for seq in self.sequences for char in [seq[0]]]\n",
+    "            for char in first_chars:\n",
+    "                nest.SetStatus(self.input_excitation_dend[char], {'spike_times': excitation_times_dend[char]})\n",
+    "\n",
+    "    def __create_recording_devices(self, excitation_times, idend_recording_times):\n",
+    "        \"\"\"Create recording devices\n",
+    "        \"\"\"\n",
+    "    \n",
+    "        # create a spike recorder for exc neurons\n",
+    "        self.spike_recorder_soma = nest.Create('spike_recorder', params={'record_to': 'ascii','label': 'somatic_spikes'})\n",
+    "        self.spike_recorder_soma_ = nest.Create('spike_recorder', params={'label': 'somatic_spikes'})\n",
+    "\n",
+    "        # create a spike recorder for inh neurons\n",
+    "        self.spike_recorder_inh = nest.Create('spike_recorder', params={'record_to': 'ascii','label': 'inh_spikes'})\n",
+    "        self.spike_recorder_inh_ = nest.Create('spike_recorder', params={'label': 'inh_spikes'})\n",
+    "\n",
+    "        # create multimeter to record dendritic currents of exc_neurons at the time of the last element in the sequence\n",
+    "        if self.params['evaluate_performance']:\n",
+    "            self.multimeter_idend_eval = nest.Create('multimeter', self.num_sequences,\n",
+    "                                                     params={'record_from': ['I_dend'],\n",
+    "                                                             'record_to': 'ascii',\n",
+    "                                                             'label': 'idend_eval'})\n",
+    "            \n",
+    "            self.multimeter_idend_eval_ = nest.Create('multimeter',\n",
+    "                                                     params={'record_from': ['I_dend'],\n",
+    "                                                             'label': 'idend_eval'})\n",
+    "            self.multimeter_vm_eval_ = nest.Create('multimeter',\n",
+    "                                                     params={'record_from': ['V_m'],\n",
+    "                                                             'label': 'vm_eval'})\n",
+    "\n",
+    "\n",
+    "            for i in range(self.num_sequences):\n",
+    "                idend_eval_spec_dict = {'offset': idend_recording_times[i][0] + self.params['idend_record_time'],\n",
+    "                                        'interval': idend_recording_times[i][1] - idend_recording_times[i][0]}\n",
+    "                nest.SetStatus(self.multimeter_idend_eval[i], idend_eval_spec_dict)\n",
+    "\n",
+    "            #nest.SetStatus(self.multimeter_idend_eval_, {\"interval\": nest.GetKernelStatus()[\"resolution\"]})\n",
+    "            #nest.SetStatus(self.multimeter_vm_eval_, {\"interval\": nest.GetKernelStatus()[\"resolution\"]})\n",
+    "\n",
+    "        # # create multimeter for recording dendritic current from all subpopulations of the last episode\n",
+    "        # if self.params['record_idend_last_episode']:\n",
+    "        #     self.multimeter_idend_last_episode = nest.Create('multimeter', params={'record_from': ['I_dend'],\n",
+    "        #                                                                            'record_to': 'ascii',\n",
+    "        #                                                                            'label': 'idend_last_episode'})\n",
+    "\n",
+    "        #     if self.params['evaluate_replay']:\n",
+    "        #         idend_dict = {'interval': self.params['idend_recording_interval'],\n",
+    "        #                       'start': self.params['excitation_start'],\n",
+    "        #                       'stop': self.params['excitation_start'] \\\n",
+    "        #                               + len(self.sequences) * self.params['DeltaT_cue']}\n",
+    "\n",
+    "        #         nest.SetStatus(self.multimeter_idend_last_episode, idend_dict)\n",
+    "        #     else:\n",
+    "        #         number_elements_per_batch = sum([len(seq) for seq in self.sequences])\n",
+    "        #         idend_dict = {'interval': self.params['idend_recording_interval'],\n",
+    "        #                       'start': excitation_times[-number_elements_per_batch],\n",
+    "        #                       'stop': excitation_times[-1] + self.params['pad_time']}\n",
+    "\n",
+    "        #         nest.SetStatus(self.multimeter_idend_last_episode, idend_dict)\n",
+    "\n",
+    "        # create multimeter for recording dendritic current from all subpopulations of the last episode\n",
+    "        self.multimeter_idend_last_episode = nest.Create('multimeter', params={'record_from': ['I_dend'],\n",
+    "                                                                               'record_to': 'ascii',\n",
+    "                                                                               'label': 'idend_last_episode'})\n",
+    "\n",
+    "        idend_dict = {'interval': self.params['idend_recording_interval'],\n",
+    "                      'start': 0.,\n",
+    "                      'stop': np.inf}\n",
+    "\n",
+    "        nest.SetStatus(self.multimeter_idend_last_episode, idend_dict)\n",
+    "\n",
+    "    def __create_weight_recorder(self):\n",
+    "        \"\"\"Create weight recorder\n",
+    "        \"\"\"\n",
+    "\n",
+    "        self.wr = nest.Create('weight_recorder', {'record_to': 'ascii', 'label': 'weight_recorder'})\n",
+    "        #self.params['syn_dict_ee']['weight_recorder'] = self.wr\n",
+    "        nest.CopyModel(params['syn_dict_ee']['synapse_model'], 'stdsp_synapse_rec', {'weight_recorder': self.wr})\n",
+    "        self.params['syn_dict_ee']['synapse_model'] = 'stdsp_synapse_rec'\n",
+    "\n",
+    "    def __compute_timing_external_inputs(self, DeltaT, DeltaT_seq, DeltaT_cue, excitation_start, time_dend_to_somatic):\n",
+    "        \"\"\"Specifies the excitation times of the external input for each sequence element,\n",
+    "        subsequent sequence elements are presented  with  inter-stimulus interval DeltaT,  \n",
+    "        subsequent sequences are separated in time by an inter-sequence time interval DeltaT_seq,\n",
+    "        during the replay, the presented cues are seperated by an intercue time interval Delta_cue,\n",
+    "        In addition this function saves the times at which a dendritic current should be recorded,\n",
+    "        we don't want to record the dendritic current every time step as this consumes a lot of memory,\n",
+    "        so we instead record the dendritic current every 'episodes_to_testing' episodes,\n",
+    "        recording the dendritic current is essential for computing the prediction performance,\n",
+    "        the dendritic current is saved only at the time of last element in the sequence,\n",
+    "        this is because when assessing the prediction performance, we compute the prediction error \n",
+    "        only with respect to the last element in the sequence\n",
+    "        \n",
+    "        Parameters\n",
+    "        ---------\n",
+    "        DeltaT               : float\n",
+    "        DeltaT_seq           : float\n",
+    "        DeltaT_cue           : float \n",
+    "        excitation_start     : float\n",
+    "        time_dend_to_somatic : float\n",
+    "\n",
+    "        Returns:\n",
+    "        --------\n",
+    "        excitation_times: list(float)\n",
+    "        excitation_times_soma: dict\n",
+    "        excitation_times_dend: dict\n",
+    "        idend_recording_times: dict\n",
+    "        \"\"\"\n",
+    "\n",
+    "        excitation_times_soma = defaultdict(list)\n",
+    "        excitation_times_dend = defaultdict(list)\n",
+    "        idend_recording_times = defaultdict(list)\n",
+    "\n",
+    "        excitation_times = []\n",
+    "        sim_time = excitation_start\n",
+    "        for le in range(self.params['learning_episodes'] + 1):\n",
+    "            print(\"Learning episode: \" + str(le) + \" of \" + str(self.params['learning_episodes'] + 1))\n",
+    "\n",
+    "            for seq_num, sequence in enumerate(self.sequences):\n",
+    "                len_seq = len(sequence)\n",
+    "                for i, char in enumerate(sequence):\n",
+    "\n",
+    "                    if i != 0:\n",
+    "                        sim_time += DeltaT\n",
+    "\n",
+    "                    # store time of excitation for each symbol\n",
+    "                    excitation_times_soma[char] += [sim_time]\n",
+    "                    if i == 0:\n",
+    "                        excitation_times_dend[char] += [sim_time - time_dend_to_somatic]\n",
+    "\n",
+    "                    # store dendritic spike times recording\n",
+    "                    if (i == len_seq - 2) and (le % self.params['episodes_to_testing'] == 0):\n",
+    "                        idend_recording_times[seq_num] += [sim_time]\n",
+    "\n",
+    "                    excitation_times.append(sim_time)\n",
+    "\n",
+    "                    if self.params['evaluate_replay']:\n",
+    "                        break\n",
+    "\n",
+    "                # set timing between sequences\n",
+    "                if self.params['evaluate_replay']:\n",
+    "                    sim_time += DeltaT_cue\n",
+    "                else:\n",
+    "                    sim_time += DeltaT_seq\n",
+    "\n",
+    "        # save data\n",
+    "        if self.params['evaluate_performance'] or self.params['evaluate_replay']:\n",
+    "\n",
+    "            np.save('idend_recording_times', idend_recording_times)\n",
+    "            print(\"Saving idend_recording_times to \" + 'idend_recording_times')\n",
+    "            np.save('excitation_times_soma',    excitation_times_soma)\n",
+    "            np.save('excitation_times', excitation_times)\n",
+    "\n",
+    "        self.sim_time = sim_time\n",
+    "        return excitation_times, [excitation_times_soma, excitation_times_dend], idend_recording_times\n",
+    "\n",
+    "    def __get_subpopulation_neurons(self, index_subpopulation):\n",
+    "        \"\"\"Get neuron's indices (NEST NodeCollection) belonging to a subpopulation\n",
+    "        \n",
+    "        Parameters\n",
+    "        ---------\n",
+    "        index_subpopulation: int\n",
+    "\n",
+    "        Returns\n",
+    "        -------\n",
+    "        NEST NodeCollection\n",
+    "        \"\"\"\n",
+    "\n",
+    "        neurons_indices = [int(index_subpopulation) * self.params['n_E'] + i for i in\n",
+    "                           range(self.params['n_E'])]\n",
+    "\n",
+    "        return self.exc_neurons[neurons_indices]\n",
+    "\n",
+    "    def __connect_excitatory_neurons(self):\n",
+    "        \"\"\"Connect excitatory neurons\n",
+    "        \"\"\"\n",
+    "        print(\"Conn exc neurons\")\n",
+    "        print(self.params['conn_dict_ee'])\n",
+    "        print(self.params['syn_dict_ee'])\n",
+    "        nest.Connect(self.exc_neurons, self.exc_neurons, conn_spec=self.params['conn_dict_ee'],\n",
+    "                     syn_spec=self.params['syn_dict_ee'])\n",
+    "#        syn = nest.GetConnections(source=self.exc_neurons, target=self.exc_neurons, synapse_model=self.params[\"syn_dict_ee\"][\"synapse_model_name\"])   # XXX\n",
+    "#        assert all(np.array(syn.weight) > 0)\n",
+    "\n",
+    "    def __connect_inhibitory_neurons(self):\n",
+    "        \"\"\"Connect inhibitory neurons\n",
+    "        \"\"\"\n",
+    "\n",
+    "        for k, subpopulation_index in enumerate(range(self.num_subpopulations)):\n",
+    "            # connect inhibitory population \n",
+    "            subpopulation_neurons = self.__get_subpopulation_neurons(subpopulation_index)\n",
+    "\n",
+    "            # connect neurons within the same mini-subpopulation to the inhibitory population\n",
+    "            nest.Connect(subpopulation_neurons, self.inh_neurons[k], syn_spec=self.params['syn_dict_ie'])\n",
+    "\n",
+    "            # connect the inhibitory neurons to the neurons within the same mini-subpopulation\n",
+    "            nest.Connect(self.inh_neurons[k], subpopulation_neurons, syn_spec=self.params['syn_dict_ei'])\n",
+    "\n",
+    "    def __connect_external_inputs_to_subpopulations(self):\n",
+    "        \"\"\"Connect external inputs to subpopulations\n",
+    "        \"\"\"\n",
+    "\n",
+    "        # get input encoding\n",
+    "        self.characters_to_subpopulations = self.__stimulus_preference(fname='characters_to_subpopulations')\n",
+    "\n",
+    "        # save characters_to_subpopulations for evaluation\n",
+    "        if self.params['evaluate_performance'] or self.params['evaluate_replay']:\n",
+    "            fname = 'characters_to_subpopulations'\n",
+    "            np.save(fname, self.characters_to_subpopulations)\n",
+    "\n",
+    "        for char in self.vocabulary:\n",
+    "            subpopulations_indices = self.characters_to_subpopulations[char]\n",
+    "\n",
+    "            # receptor type 1 correspond to the feedforward synapse of the 'iaf_psc_exp_multisynapse' model\n",
+    "            for subpopulation_index in subpopulations_indices:\n",
+    "                subpopulation_neurons = self.__get_subpopulation_neurons(subpopulation_index)\n",
+    "                nest.Connect(self.input_excitation_soma[char], subpopulation_neurons,\n",
+    "                             self.params['conn_dict_ex'], syn_spec=self.params['syn_dict_ex'])\n",
+    "                nest.Connect(self.input_excitation_dend[char], subpopulation_neurons,\n",
+    "                             self.params['conn_dict_edx'], syn_spec=self.params['syn_dict_edx'])\n",
+    "\n",
+    "    def __stimulus_preference(self, fname='characters_to_subpopulations'):\n",
+    "        \"\"\"Assign a subset of subpopulations to a each element in the vocabulary.\n",
+    "\n",
+    "        Parameters\n",
+    "        ----------\n",
+    "        fname : str\n",
+    "\n",
+    "        Returns\n",
+    "        -------\n",
+    "        characters_to_subpopulations: dict\n",
+    "        \"\"\"\n",
+    "\n",
+    "        if len(self.vocabulary) * self.params['L'] > self.num_subpopulations:\n",
+    "            raise ValueError(\n",
+    "                \"num_subpopulations needs to be large than length_user_characters*num_subpopulations_per_character\")\n",
+    "\n",
+    "        characters_to_subpopulations = defaultdict(list)  # a dictionary that assigns mini-subpopulation to characters\n",
+    "\n",
+    "        subpopulation_indices = np.arange(self.num_subpopulations)\n",
+    "        # permuted_subpopulation_indices = np.random.permutation(subpopulation_indices)\n",
+    "        permuted_subpopulation_indices = subpopulation_indices\n",
+    "        index_characters_to_subpopulations = []\n",
+    "\n",
+    "        if self.params['load_connections']:\n",
+    "            # load connectivity: from characters to mini-subpopulations\n",
+    "            \n",
+    "            characters_to_subpopulations = load_input_encoding( fname)\n",
+    "        else:\n",
+    "            for char in self.vocabulary:\n",
+    "                # randomly select a subset of mini-subpopulations for a character\n",
+    "                characters_to_subpopulations[char] = permuted_subpopulation_indices[:self.params['L']]\n",
+    "                # delete mini-subpopulations from the permuted_subpopulation_indices that are already selected\n",
+    "                permuted_subpopulation_indices = permuted_subpopulation_indices[self.params['L']:]\n",
+    "\n",
+    "        return characters_to_subpopulations\n",
+    "\n",
+    "    def __set_min_synaptic_strength(self):\n",
+    "        \"\"\"Set synaptic Wmin\n",
+    "        \"\"\"\n",
+    "\n",
+    "        connections = nest.GetConnections(synapse_model=self.params['syn_dict_ee']['synapse_model'])\n",
+    "\n",
+    "        if \"stdsp\" in self.params['syn_dict_ee']['synapse_model']:\n",
+    "            if \"synapse_nestml\" in synapse_model_name:\n",
+    "                connections.set({'permanence_min': connections.permanence})\n",
+    "            else:\n",
+    "                connections.set({'Pmin': connections.permanence})\n",
+    "        else:\n",
+    "            assert np.unique(connections.synapse_model)[0] == \"static_synapse\"\n",
+    "            connections.set({'Wmin': connections.weight})\n",
+    "\n",
+    "    def save_connections(self, fname='ee_connections'):\n",
+    "        \"\"\"Save connection matrix\n",
+    "\n",
+    "        Parameters\n",
+    "        ----------\n",
+    "        label: str\n",
+    "            name of the stored file\n",
+    "        \"\"\"\n",
+    "\n",
+    "        print('\\nSave connections to ' + '%s' % fname + '...')\n",
+    "        connections_all = nest.GetConnections(synapse_model=self.params['syn_dict_ee']['synapse_model'])\n",
+    "\n",
+    "        connections = nest.GetStatus(connections_all, ['target', 'source', 'weight', 'permanence'])\n",
+    "\n",
+    "        np.save(fname, connections)\n",
+    "        print('\\n -> finished saving connections!')\n",
+    "        \n",
+    "    def __load_connections(self, label='ee_connections'):\n",
+    "        \"\"\"Load connection matrix\n",
+    "        \n",
+    "        Parameters\n",
+    "        ----------\n",
+    "        label: str\n",
+    "            name of the stored file\n",
+    "        \"\"\"\n",
+    "\n",
+    "        assert self.params['syn_dict_ee']['synapse_model'] != 'stdsp_synapse_rec', \"synapse model not tested yet\"\n",
+    "\n",
+    "        print('Load connections from ' + label + '...')\n",
+    "        conns = np.load('%s.npy' % label)\n",
+    "        conns_tg = [int(conn[0]) for conn in conns]\n",
+    "        conns_src = [int(conn[1]) for conn in conns]\n",
+    "        conns_weights = [conn[2] for conn in conns]\n",
+    "\n",
+    "        if \"stdsp\" in self.params['syn_dict_ee']['synapse_model']:\n",
+    "            conns_perms = [conn[3] for conn in conns]\n",
+    "\n",
+    "        if self.params['evaluate_replay']:\n",
+    "            print(\"\\tEvaluate replay, using static synapses\")\n",
+    "            syn_dict = {'receptor_type': 2,\n",
+    "                        'delay': [self.params['syn_dict_ee']['delay']] * len(conns_weights),\n",
+    "                        'weight': conns_weights}\n",
+    "            nest.Connect(conns_src, conns_tg, 'one_to_one', syn_dict)\n",
+    "        else:\n",
+    "            print(\"\\tUsing synapse model: \" + self.params['syn_dict_ee']['synapse_model'])\n",
+    "            \n",
+    "            syn_dict_ee = copy.deepcopy(self.params['syn_dict_ee'])\n",
+    "\n",
+    "            del syn_dict_ee['synapse_model']\n",
+    "            del syn_dict_ee['weight']\n",
+    "            del syn_dict_ee['receptor_type']\n",
+    "            if \"stdsp\" in self.params['syn_dict_ee']['synapse_model']:\n",
+    "                del syn_dict_ee['permanence']\n",
+    "\n",
+    "            nest.SetDefaults(self.params['syn_dict_ee']['synapse_model'], syn_dict_ee)\n",
+    "\n",
+    "            if \"stdsp\" in self.params['syn_dict_ee']['synapse_model']:\n",
+    "                syn_dict = {'synapse_model': self.params['syn_dict_ee']['synapse_model'],\n",
+    "                            'receptor_type': 2,\n",
+    "                            'weight': conns_weights,\n",
+    "                            'permanence': conns_perms}\n",
+    "            else:\n",
+    "                syn_dict = {'synapse_model': self.params['syn_dict_ee']['synapse_model'],\n",
+    "                            'receptor_type': 2,\n",
+    "                            'weight': conns_weights}\n",
+    "\n",
+    "            nest.Connect(conns_src, conns_tg, 'one_to_one', syn_dict)\n",
+    "\n",
+    "    def __get_time_constant_dendritic_rate(self, DeltaT=40., DeltaT_seq=100., calibration=100, target_firing_rate=1):\n",
+    "        \"\"\"Compute time constant of the dendritic AP rate,\n",
+    "\n",
+    "        The time constant is set such that the rate captures how many dAPs a neuron generated\n",
+    "        all along the period of a batch\n",
+    "         \n",
+    "        Parameters\n",
+    "        ----------\n",
+    "        calibration : float\n",
+    "        target_firing_rate : float\n",
+    "\n",
+    "        Returns\n",
+    "        -------\n",
+    "        float\n",
+    "           time constant of the dendritic AP rate\n",
+    "        \"\"\"\n",
+    "\n",
+    "        t_exc = ((self.length_sequence-1) * DeltaT + DeltaT_seq + calibration) \\\n",
+    "                * self.num_sequences\n",
+    "\n",
+    "        print(\"\\nDuration of a sequence set %d ms\" % t_exc)\n",
+    "\n",
+    "        return target_firing_rate * t_exc\n",
+    "\n",
+    "\n",
+    "###########################################\n",
+    "def load_input_encoding( fname):\n",
+    "    \"\"\"Load input encoding: association between sequence element and subpopulations\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    path: str\n",
+    "    fname: str\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    characters_to_subpopulations: dict\n",
+    "    \"\"\"\n",
+    "\n",
+    "    characters_to_subpopulations = load_data( fname)\n",
+    "\n",
+    "    return characters_to_subpopulations\n",
+    "\n",
+    "def clear_recorded_data():\n",
+    "    import glob\n",
+    "    files = glob.glob(\"somatic_spikes*dat\")\n",
+    "    files += glob.glob(\"inh_spikes*dat\")\n",
+    "    files += glob.glob(\"idend_eval*dat\")\n",
+    "    files += glob.glob(\"idend_last_episode*dat\")\n",
+    "    for file in files:\n",
+    "        try:\n",
+    "            os.remove(file)\n",
+    "            print(f\"Removed: {file}\")\n",
+    "        except Exception as e:\n",
+    "            print(f\"Error removing {file}: {e}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_sequence_learning_parameters():\n",
+    "    DELAY = 0.1\n",
+    "\n",
+    "    p = para.ParameterSpace({})\n",
+    "    \n",
+    "    p['dt'] = 0.1                                  # simulation time resolution (ms)\n",
+    "    p['print_simulation_progress'] = False         # print the time progress -- True might cause issues with Jupyter\n",
+    "    \n",
+    "    # neuron parameters of the excitatory neurons\n",
+    "    p['soma_model'] = neuron_model_name\n",
+    "    p['soma_params'] = {}\n",
+    "    p['soma_params']['C_m'] = 250.        # membrane capacitance (pF)\n",
+    "    p['soma_params']['E_L'] = 0.          # resting membrane potential (mV)\n",
+    "    # p['soma_params']['I_e'] = 0.        # external DC currents (pA)\n",
+    "    p['soma_params']['V_m'] = 0.          # initial potential (mV)\n",
+    "    p['soma_params']['V_reset'] = 0.      # reset potential (mV)\n",
+    "    p['soma_params']['V_th'] = 20.        # spike threshold (mV)\n",
+    "    p['soma_params']['t_ref'] = 10.       # refractory period\n",
+    "    p['soma_params']['tau_m'] = 10.       # membrane time constant (ms)\n",
+    "    p['soma_params']['tau_syn1'] = 2.     # synaptic time constant: external input (receptor 1)\n",
+    "    p['soma_params']['tau_syn2'] = 5.     # synaptic time constant: dendrtic input (receptor 2)\n",
+    "    p['soma_params']['tau_syn3'] = 1.     # synaptic time constant: inhibitory input (receptor 3)\n",
+    "    # dendritic action potential\n",
+    "    p['soma_params']['I_p'] = 200. # current clamp value for I_dAP during a dendritic action potenti\n",
+    "    p['soma_params']['tau_dAP'] = 60.       # time window over which the dendritic current clamp is active\n",
+    "    p['soma_params']['theta_dAP'] = 59.        # current threshold for a dendritic action potential\n",
+    "    \n",
+    "    p['soma_params']['I_dend_incr'] = 2.71 / (p['soma_params']['tau_syn2'])\n",
+    "    \n",
+    "    \n",
+    "    p['fixed_somatic_delay'] = 2          # this is an approximate time of how long it takes the soma to fire\n",
+    "                                          # upon receiving an external stimulus \n",
+    "        \n",
+    "    # parameters for setting up the network  \n",
+    "    p['M'] = 6                   # number of subpopulations\n",
+    "    p['n_E'] = 150               # number of excitatory neurons per subpopulation\n",
+    "    p['n_I'] = 1                 # number of inhibitory neurons per subpopulation\n",
+    "    p['L'] = 1                   # number of subpopulations that represents one sequence element\n",
+    "    p['pattern_size'] = 20       # sparse set of active neurons per subpopulation\n",
+    "    \n",
+    "    # neuron parameters for the inhibitory neuron\n",
+    "    p['inhibit_model'] = 'iaf_psc_exp'\n",
+    "    p['inhibit_params'] = {}\n",
+    "    p['inhibit_params']['C_m'] = 250.         # membrane capacitance (pF)\n",
+    "    p['inhibit_params']['E_L'] = 0.           # resting membrane potential (mV)\n",
+    "    p['inhibit_params']['I_e'] = 0.           # external DC currents (pA)\n",
+    "    p['inhibit_params']['V_m'] = 0.           # initial potential (mV)\n",
+    "    p['inhibit_params']['V_reset'] = 0.       # reset potential (mV)\n",
+    "    p['inhibit_params']['V_th'] = 15.         # spike threshold (mV)\n",
+    "    p['inhibit_params']['t_ref'] = 2.0        # refractory period\n",
+    "    p['inhibit_params']['tau_m'] = 5.         # membrane time constant (ms)\n",
+    "    p['inhibit_params']['tau_syn_ex'] = .5    # synaptic time constant of an excitatory input (ms) \n",
+    "    p['inhibit_params']['tau_syn_in'] = 1.65  # synaptic time constant of an inhibitory input (ms)\n",
+    "        \n",
+    "    # synaptic parameters\n",
+    "    p['J_EX_psp'] = 1.1 * p['soma_params']['V_th']     # somatic PSP as a response to an external input\n",
+    "    p['J_IE_psp'] = 1.2 * p['inhibit_params']['V_th']  # inhibitory PSP as a response to an input from E neuron\n",
+    "    \n",
+    "    # XXXXXXXXXXX\n",
+    "#     if params['evaluate_replay']:\n",
+    "#         params['J_IE_psp'] /= params['n_E']\n",
+    "#     else:\n",
+    "#         params['J_IE_psp'] /= params['pattern_size']\n",
+    "    p['J_IE_psp'] /= p['pattern_size']\n",
+    "\n",
+    "    \n",
+    "    p['J_EI_psp'] = -2 * p['soma_params']['V_th']      # somatic PSP as a response to an inhibitory input\n",
+    "    p['convergence'] = 5\n",
+    "    \n",
+    "    # connection details\n",
+    "    p['rule'] = 'fixed_indegree'                          \n",
+    "    p['connection_prob'] = 0.2\n",
+    "    \n",
+    "    # parameters for ee synapses (stdsp)\n",
+    "    p['syn_dict_ee'] = {}\n",
+    "    p['permanence_min'] = 0.\n",
+    "    p['permanence_max'] = 8.\n",
+    "\n",
+    "    p['calibration'] = 0.\n",
+    "    p['syn_dict_ee']['weight'] = 0.01                    # synaptic weight\n",
+    "    p['syn_dict_ee']['synapse_model'] = synapse_model_name  # synapse model\n",
+    "    if \"synapse_nestml\" in synapse_model_name:\n",
+    "        p['syn_dict_ee']['permanence_threshold'] = 10.                    # synapse maturity threshold\n",
+    "        p['syn_dict_ee']['tau_pre_trace'] = 20.                   # plasticity time constant (potentiation)\n",
+    "    else:\n",
+    "        p['syn_dict_ee']['th_perm'] = 10.                    # synapse maturity threshold\n",
+    "        p['syn_dict_ee']['tau_plus'] = 20.                   # plasticity time constant (potentiation)\n",
+    "    p['syn_dict_ee']['delay'] = 2.                       # dendritic delay \n",
+    "    p['syn_dict_ee']['receptor_type'] = 2                # receptor corresponding to the dendritic input\n",
+    "    p['syn_dict_ee']['lambda_plus'] = 0.08               # potentiation rate\n",
+    "    p['syn_dict_ee']['zt'] = 1.                          # target dAP trace\n",
+    "    p['syn_dict_ee']['lambda_h'] = 0.014                 # homeostasis rate\n",
+    "    p['syn_dict_ee']['Wmax'] = 1.1 * p['soma_params']['theta_dAP'] / p['convergence']   # Maximum allowed weight\n",
+    "    if \"synapse_nestml\" in synapse_model_name:\n",
+    "        p['syn_dict_ee']['permanence_max'] = 20.                       # Maximum allowed permanence\n",
+    "        p['syn_dict_ee']['permanence_min'] = 1.                        # Minimum allowed permanence\n",
+    "    else:\n",
+    "        p['syn_dict_ee']['Pmax'] = 20.                       # Maximum allowed permanence\n",
+    "        p['syn_dict_ee']['Pmin'] = 1.                        # Minimum allowed permanence\n",
+    "    p['syn_dict_ee']['lambda_minus'] = 0.0015            # depression rate\n",
+    "    if \"synapse_nestml\" in synapse_model_name:\n",
+    "        p['syn_dict_ee']['dt_min'] = 4.                     # minimum time lag of the STDP window\n",
+    "    else:\n",
+    "        p['syn_dict_ee']['dt_min'] = -4.                     # minimum time lag of the STDP window\n",
+    "    \n",
+    "    # parameters of EX synapses (external to soma of E neurons)\n",
+    "    p['conn_dict_ex'] = {}\n",
+    "    p['syn_dict_ex'] = {}\n",
+    "    p['syn_dict_ex']['receptor_type'] = 1                    # receptor corresponding to external input\n",
+    "    p['syn_dict_ex']['delay'] = DELAY                        # dendritic delay\n",
+    "    p['conn_dict_ex']['rule'] = 'all_to_all'                 # connection rule\n",
+    "    \n",
+    "    # parameters of EdX synapses (external to dendrite of E neurons) \n",
+    "    p['conn_dict_edx'] = {}\n",
+    "    p['syn_dict_edx'] = {}\n",
+    "    p['syn_dict_edx']['receptor_type'] = 2                    # receptor corresponding to the dendritic input\n",
+    "    p['syn_dict_edx']['delay'] = DELAY                        # dendritic delay\n",
+    "    p['syn_dict_edx']['weight'] = 1.4 * p['soma_params']['theta_dAP']\n",
+    "    p['conn_dict_edx']['rule'] = 'fixed_outdegree'            # connection rule\n",
+    "    p['conn_dict_edx']['outdegree'] = p['pattern_size'] + 1   # outdegree\n",
+    "    \n",
+    "    # parameters for IE synapses \n",
+    "    p['syn_dict_ie'] = {}\n",
+    "    #p['conn_dict_ie'] = {}\n",
+    "    p['syn_dict_ie']['synapse_model'] = 'static_synapse'     # synapse model\n",
+    "    p['syn_dict_ie']['delay'] = DELAY                        # dendritic delay\n",
+    "    #p['conn_dict_ie']['rule'] = 'fixed_indegree'             # connection rule\n",
+    "    #p['conn_dict_ie']['indegree'] = 5                        # indegree \n",
+    "    \n",
+    "    # parameters for EI synapses\n",
+    "    p['syn_dict_ei'] = {}\n",
+    "    #p['conn_dict_ei'] = {}\n",
+    "    p['syn_dict_ei']['synapse_model'] = 'static_synapse'     # synapse model\n",
+    "    p['syn_dict_ei']['delay'] = DELAY                        # dendritic delay\n",
+    "    p['syn_dict_ei']['receptor_type'] = 3                    # receptor corresponding to the inhibitory input  \n",
+    "    #p['conn_dict_ei']['rule'] = 'fixed_indegree'             # connection rule\n",
+    "    #p['conn_dict_ei']['indegree'] = 20                       # indegree\n",
+    "    \n",
+    "    # stimulus parameters\n",
+    "    p['DeltaT'] = 40.                     # inter-stimulus interval\n",
+    "    p['excitation_start'] = 30.           # time at which the external stimulation begins\n",
+    "    p['time_dend_to_somatic'] = 20.       # time between the dAP activation and the somatic activation (only used if sparse_first_char is True)   \n",
+    "    p['DeltaT_cue'] = 80.                 # inter-cue interval during replay\n",
+    "    \n",
+    "    # simulation parameters \n",
+    "    p['dt'] = 0.1                                  # simulation time resolution (ms)\n",
+    "    p['overwrite_files'] = True                    # if True, data will be overwritten,\n",
+    "                                                   # if False, a NESTError is raised if the files already exist\n",
+    "    p['seed'] = 111   # seed for NEST\n",
+    "    p['pad_time'] = 5.\n",
+    "    p['idend_recording_interval'] = 10 * p['dt']   # dendritic current recording resolution\n",
+    "    p['idend_record_time'] = 8.                    # time interval after the external stimulation at which the dendritic current is recorded\n",
+    "    p['evaluate_performance'] = True               # if turned on, we monitor the dendritic current at a certain time steps\n",
+    "                                                   # during the simulation. This then is used for the prediction performance assessment\n",
+    "    p['evaluate_replay'] = False                     \n",
+    "    p['record_idend_last_episode'] = True          # used for debugging, if turned on we record the dendritic current of all neurons\n",
+    "                                                   # this can consume too much memory\n",
+    "    p['store_connections'] = False              \n",
+    "    p['load_connections'] = False\n",
+    "    p['sparse_first_char'] = False                 # if turned on, the dAP of a subset of neurons in the subpopulation representing \n",
+    "                                                   # first sequence elements is activated externally \n",
+    "    p['active_weight_recorder'] = False            # if turned on, the weights are recorded every presynaptic spike\n",
+    "    \n",
+    "    # task parameters\n",
+    "    p['task'] = {}\n",
+    "    p['task']['task_name'] = 'hard_coded'          # name of the task\n",
+    "    p['task']['task_type'] = 1                     # this chooses between three hard coded sequence sets (see ./utils.py)\n",
+    "    p['task']['vocab_size'] = 6                    # vocabulary size\n",
+    "    p['task']['seed'] = 111                        # seed number\n",
+    "    p['task']['store_training_data'] = True        # if turned on, the sequence set is stored \n",
+    "    \n",
+    "    p['learning_episodes'] = 100                    # total number of training episodes ('repetitions of the sequence sets')\n",
+    "\n",
+    "    # ----------------------------------\n",
+    "    # task parameters: alternative\n",
+    "    # IMPOSSIBLE TASK! First and last elements overlap between different sequences\n",
+    " \n",
+    "#     p['task'] = {}\n",
+    "#     p['task']['task_name'] = 'hard_coded'          # name of the task\n",
+    "#     p['task']['task_type'] = 6                     # this chooses between three hard coded sequence sets (see ./utils.py)\n",
+    "#     p['task']['vocab_size'] = 7                    # vocabulary size\n",
+    "#     p['task']['store_training_data'] = True        # if turned on, the sequence set is stored \n",
+    "#     p['M'] = p['task']['vocab_size']               # number of subpopulations\n",
+    "\n",
+    "#     p['learning_episodes'] = 50                    # total number of training episodes ('repetitions of the sequence sets')\n",
+    "    # ----------------------------------\n",
+    "    \n",
+    "    # setup the training loop  \n",
+    "    p['episodes_to_testing'] = 1                   # number of episodes after which we measure the prediction perfomance\n",
+    "\n",
+    "    if \"synapse_nestml\" not in synapse_model_name:\n",
+    "        p['mu_plus']= 0.0 \n",
+    "        p['mu_minus']= 0.0\n",
+    "    \n",
+    "    return p\n",
+    "\n",
+    "\n",
+    "params = create_sequence_learning_parameters()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Generate the vocabulary and the sequences to learn:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Save training data to training_data\n",
+      "Vocabulary: ['A', 'B', 'C', 'D', 'E', 'F']\n",
+      "Sequences: [['A', 'D', 'B', 'E'], ['F', 'D', 'B', 'C']]\n"
+     ]
+    }
+   ],
+   "source": [
+    "sequences, _, vocabulary = generate_sequences(params['task'], fname=\"sequences\")\n",
+    "\n",
+    "print(\"Vocabulary: \" + str(vocabulary))\n",
+    "print(\"Sequences: \" + str(sequences))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Initialising model and simulation...\n",
+      "\n",
+      "#### postsynaptic potential ####\n",
+      "PSP maximum J_EX psp:  22.000000 mV\n",
+      "PSP maximum J_IE psp:  0.900000 mV\n",
+      "PSP maximum J_EI psp:  -40.000000 mV\n",
+      "\n",
+      "#### postsynaptic current ####\n",
+      "PSC maximum J_EX:  4112.209148 pA\n",
+      "PSC maximum J_IE:  581.197349 pA\n",
+      "PSC maximum J_EI:  -12915.496650 pA\n",
+      "Model parameters: {'dt': 0.1, 'print_simulation_progress': False, 'soma_model': 'iaf_psc_exp_nonlineardendrite_neuron_nestml__with_stdsp_synapse_nestml', 'soma_params': {'C_m': 250.0, 'E_L': 0.0, 'V_m': 0.0, 'V_reset': 0.0, 'V_th': 20.0, 't_ref': 10.0, 'tau_m': 10.0, 'tau_syn1': 2.0, 'tau_syn2': 5.0, 'tau_syn3': 1.0, 'I_p': 200.0, 'tau_dAP': 60.0, 'theta_dAP': 59.0, 'I_dend_incr': 0.542}, 'fixed_somatic_delay': 2, 'M': 6, 'n_E': 150, 'n_I': 1, 'L': 1, 'pattern_size': 20, 'inhibit_model': 'iaf_psc_exp', 'inhibit_params': {'C_m': 250.0, 'E_L': 0.0, 'I_e': 0.0, 'V_m': 0.0, 'V_reset': 0.0, 'V_th': 15.0, 't_ref': 2.0, 'tau_m': 5.0, 'tau_syn_ex': 0.5, 'tau_syn_in': 1.65}, 'J_EX_psp': 22.0, 'J_IE_psp': 0.9, 'J_EI_psp': -40.0, 'convergence': 5, 'rule': 'fixed_indegree', 'connection_prob': 0.2, 'syn_dict_ee': {'weight': 0.01, 'synapse_model': 'stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml', 'permanence_threshold': 10.0, 'tau_pre_trace': 20.0, 'delay': 2.0, 'receptor_type': 2, 'lambda_plus': 0.08, 'zt': 1.0, 'lambda_h': 0.014, 'Wmax': 12.98, 'permanence_max': 20.0, 'permanence_min': 1.0, 'lambda_minus': 0.0015, 'dt_min': 4.0, 'permanence': <nest.lib.hl_api_types.Parameter object at 0x7f278ad4fd10>, 'dt_max': 80.0}, 'permanence_min': 0.0, 'permanence_max': 8.0, 'calibration': 0.0, 'conn_dict_ex': {'rule': 'all_to_all'}, 'syn_dict_ex': {'receptor_type': 1, 'delay': 0.1, 'weight': 4112.209148358356}, 'conn_dict_edx': {'rule': 'fixed_outdegree', 'outdegree': 21}, 'syn_dict_edx': {'receptor_type': 2, 'delay': 0.1, 'weight': 82.6}, 'syn_dict_ie': {'synapse_model': 'static_synapse', 'delay': 0.1, 'weight': 581.1973492566976}, 'syn_dict_ei': {'synapse_model': 'static_synapse', 'delay': 0.1, 'receptor_type': 3, 'weight': -12915.496650148836}, 'DeltaT': 40.0, 'excitation_start': 30.0, 'time_dend_to_somatic': 20.0, 'DeltaT_cue': 80.0, 'overwrite_files': True, 'seed': 111, 'pad_time': 5.0, 'idend_recording_interval': 1.0, 'idend_record_time': 8.0, 'evaluate_performance': True, 'evaluate_replay': False, 'record_idend_last_episode': True, 'store_connections': True, 'load_connections': False, 'sparse_first_char': False, 'active_weight_recorder': False, 'task': {'task_name': 'hard_coded', 'task_type': 1, 'vocab_size': 6, 'seed': 111, 'store_training_data': True}, 'learning_episodes': 100, 'episodes_to_testing': 1, 'label': '24019775dabcff5b9e4149746d52090b', 'conn_dict_ee': {'rule': 'fixed_indegree', 'indegree': 180, 'allow_autapses': False, 'allow_multapses': False}, 'R_m_soma': 0.04, 'R_m_inhibit': 0.02, 'DeltaT_seq': 100.0}\n",
+      "\n",
+      "Duration of a sequence set 440 ms\n",
+      "\n",
+      "Creating and configuring nodes...\n",
+      "Learning episode: 0 of 101\n",
+      "Learning episode: 1 of 101\n",
+      "Learning episode: 2 of 101\n",
+      "Learning episode: 3 of 101\n",
+      "Learning episode: 4 of 101\n",
+      "Learning episode: 5 of 101\n",
+      "Learning episode: 6 of 101\n",
+      "Learning episode: 7 of 101\n",
+      "Learning episode: 8 of 101\n",
+      "Learning episode: 9 of 101\n",
+      "Learning episode: 10 of 101\n",
+      "Learning episode: 11 of 101\n",
+      "Learning episode: 12 of 101\n",
+      "Learning episode: 13 of 101\n",
+      "Learning episode: 14 of 101\n",
+      "Learning episode: 15 of 101\n",
+      "Learning episode: 16 of 101\n",
+      "Learning episode: 17 of 101\n",
+      "Learning episode: 18 of 101\n",
+      "Learning episode: 19 of 101\n",
+      "Learning episode: 20 of 101\n",
+      "Learning episode: 21 of 101\n",
+      "Learning episode: 22 of 101\n",
+      "Learning episode: 23 of 101\n",
+      "Learning episode: 24 of 101\n",
+      "Learning episode: 25 of 101\n",
+      "Learning episode: 26 of 101\n",
+      "Learning episode: 27 of 101\n",
+      "Learning episode: 28 of 101\n",
+      "Learning episode: 29 of 101\n",
+      "Learning episode: 30 of 101\n",
+      "Learning episode: 31 of 101\n",
+      "Learning episode: 32 of 101\n",
+      "Learning episode: 33 of 101\n",
+      "Learning episode: 34 of 101\n",
+      "Learning episode: 35 of 101\n",
+      "Learning episode: 36 of 101\n",
+      "Learning episode: 37 of 101\n",
+      "Learning episode: 38 of 101\n",
+      "Learning episode: 39 of 101\n",
+      "Learning episode: 40 of 101\n",
+      "Learning episode: 41 of 101\n",
+      "Learning episode: 42 of 101\n",
+      "Learning episode: 43 of 101\n",
+      "Learning episode: 44 of 101\n",
+      "Learning episode: 45 of 101\n",
+      "Learning episode: 46 of 101\n",
+      "Learning episode: 47 of 101\n",
+      "Learning episode: 48 of 101\n",
+      "Learning episode: 49 of 101\n",
+      "Learning episode: 50 of 101\n",
+      "Learning episode: 51 of 101\n",
+      "Learning episode: 52 of 101\n",
+      "Learning episode: 53 of 101\n",
+      "Learning episode: 54 of 101\n",
+      "Learning episode: 55 of 101\n",
+      "Learning episode: 56 of 101\n",
+      "Learning episode: 57 of 101\n",
+      "Learning episode: 58 of 101\n",
+      "Learning episode: 59 of 101\n",
+      "Learning episode: 60 of 101\n",
+      "Learning episode: 61 of 101\n",
+      "Learning episode: 62 of 101\n",
+      "Learning episode: 63 of 101\n",
+      "Learning episode: 64 of 101\n",
+      "Learning episode: 65 of 101\n",
+      "Learning episode: 66 of 101\n",
+      "Learning episode: 67 of 101\n",
+      "Learning episode: 68 of 101\n",
+      "Learning episode: 69 of 101\n",
+      "Learning episode: 70 of 101\n",
+      "Learning episode: 71 of 101\n",
+      "Learning episode: 72 of 101\n",
+      "Learning episode: 73 of 101\n",
+      "Learning episode: 74 of 101\n",
+      "Learning episode: 75 of 101\n",
+      "Learning episode: 76 of 101\n",
+      "Learning episode: 77 of 101\n",
+      "Learning episode: 78 of 101\n",
+      "Learning episode: 79 of 101\n",
+      "Learning episode: 80 of 101\n",
+      "Learning episode: 81 of 101\n",
+      "Learning episode: 82 of 101\n",
+      "Learning episode: 83 of 101\n",
+      "Learning episode: 84 of 101\n",
+      "Learning episode: 85 of 101\n",
+      "Learning episode: 86 of 101\n",
+      "Learning episode: 87 of 101\n",
+      "Learning episode: 88 of 101\n",
+      "Learning episode: 89 of 101\n",
+      "Learning episode: 90 of 101\n",
+      "Learning episode: 91 of 101\n",
+      "Learning episode: 92 of 101\n",
+      "Learning episode: 93 of 101\n",
+      "Learning episode: 94 of 101\n",
+      "Learning episode: 95 of 101\n",
+      "Learning episode: 96 of 101\n",
+      "Learning episode: 97 of 101\n",
+      "Learning episode: 98 of 101\n",
+      "Learning episode: 99 of 101\n",
+      "Learning episode: 100 of 101\n",
+      "Saving idend_recording_times to idend_recording_times\n",
+      "connect().....\n",
+      "\n",
+      "Connecting network and devices...\n",
+      "\tCreating new random connections\n",
+      "Conn exc neurons\n",
+      "{'rule': 'fixed_indegree', 'indegree': 180, 'allow_autapses': False, 'allow_multapses': False}\n",
+      "{'weight': 0.01, 'synapse_model': 'stdsp_synapse_nestml__with_iaf_psc_exp_nonlineardendrite_neuron_nestml', 'permanence_threshold': 10.0, 'tau_pre_trace': 20.0, 'delay': 2.0, 'receptor_type': 2, 'lambda_plus': 0.08, 'zt': 1.0, 'lambda_h': 0.014, 'Wmax': 12.98, 'permanence_max': 20.0, 'permanence_min': 1.0, 'lambda_minus': 0.0015, 'dt_min': 4.0, 'permanence': <nest.lib.hl_api_types.Parameter object at 0x7f278ad4fd10>, 'dt_max': 80.0}\n",
+      "connect()ed\n",
+      "Store connections.....\n",
+      "\n",
+      "Save connections to ee_connections_before...\n"
+     ]
+    }
+   ],
+   "source": [
+    "def simulate_train_network(params):\n",
+    "\n",
+    "    #############################################################\n",
+    "    # get network and training parameters \n",
+    "    # ===========================================================\n",
+    "    p = copy.deepcopy(params)\n",
+    "    PS = copy.deepcopy(p)\n",
+    "    PL = parameter_set_list(PS) \n",
+    "    params = PL[0]\n",
+    "\n",
+    "    # start time \n",
+    "    time_start = time.time()\n",
+    "\n",
+    "    # ###############################################################\n",
+    "    # create network\n",
+    "    # ===============================================================\n",
+    "    model_instance = Model(params, sequences, vocabulary)\n",
+    "    time_model = time.time()\n",
+    "    model_instance.create()\n",
+    "    time_create = time.time()\n",
+    "    print(\"connect().....\")\n",
+    "    model_instance.connect()\n",
+    "    print(\"connect()ed\")\n",
+    "    time_connect = time.time()\n",
+    "    \n",
+    "    # store connections before learning\n",
+    "    print(\"Store connections.....\")\n",
+    "    if params['store_connections']:\n",
+    "        model_instance.save_connections(fname='ee_connections_before')\n",
+    "\n",
+    "    # ###############################################################\n",
+    "    # simulate the network\n",
+    "    # ===============================================================\n",
+    "    print(\"Simulating.....\")\n",
+    "    clear_recorded_data()\n",
+    "    model_instance.simulate()\n",
+    "    time_simulate = time.time()\n",
+    "\n",
+    "    print(\"Store connections.....\")\n",
+    "    # store connections after learning\n",
+    "    if params['store_connections']:\n",
+    "        model_instance.save_connections(fname='ee_connections')\n",
+    "\n",
+    "    print(\n",
+    "        '\\nTimes of Rank {}:\\n'.format(\n",
+    "            nest.Rank()) +\n",
+    "        '  Total time:          {:.3f} s\\n'.format(\n",
+    "            time_simulate -\n",
+    "            time_start) +\n",
+    "        '  Time to initialize:  {:.3f} s\\n'.format(\n",
+    "            time_model -\n",
+    "            time_start) +\n",
+    "        '  Time to create:      {:.3f} s\\n'.format(\n",
+    "            time_create -\n",
+    "            time_model) +\n",
+    "        '  Time to connect:     {:.3f} s\\n'.format(\n",
+    "            time_connect -\n",
+    "            time_create) +\n",
+    "        '  Time to simulate:    {:.3f} s\\n'.format(\n",
+    "            time_simulate -\n",
+    "            time_connect))\n",
+    "\n",
+    "    #\n",
+    "    # PLOTTING\n",
+    "    #\n",
+    "\n",
+    "    nest.raster_plot.from_device(model_instance.spike_recorder_soma)\n",
+    "    fname_snip = str(time.time())\n",
+    "    plt.savefig(\"/tmp/nestml_raster_\" + fname_snip + \".png\")\n",
+    "\n",
+    "    for gid in [1, 100, 200]:\n",
+    "        events = model_instance.spike_recorder_soma_.get()[\"events\"]\n",
+    "        times = events[\"times\"]\n",
+    "        senders = events[\"senders\"]\n",
+    "        idx = np.where(senders == gid)[0]\n",
+    "        spike_times = events[\"times\"][idx]\n",
+    "\n",
+    "        events = model_instance.multimeter_vm_eval_.get()[\"events\"]\n",
+    "        times = events[\"times\"]\n",
+    "        senders = events[\"senders\"]\n",
+    "        idx = np.where(senders == gid)[0]\n",
+    "        V_m = events[\"V_m\"][idx]\n",
+    "        times = times[idx]\n",
+    "        assert len(times) > 100\n",
+    "\n",
+    "        \n",
+    "        fig, ax = plt.subplots()\n",
+    "        ax.plot(times, V_m)\n",
+    "        ax.scatter(spike_times, np.zeros_like(spike_times), marker=\"D\", alpha=.5)\n",
+    "        ax.set_ylabel(\"V_m\")\n",
+    "        fig.savefig(\"/tmp/nestml_V_m_\" + str(gid) + \"_\" + fname_snip + \".png\")\n",
+    "    \n",
+    "        events = model_instance.multimeter_idend_eval_.get()[\"events\"]\n",
+    "        times = events[\"times\"]\n",
+    "        senders = events[\"senders\"]\n",
+    "        idx = np.where(senders == gid)[0]\n",
+    "        I_dend = events[\"I_dend\"][idx]\n",
+    "        times = times[idx]\n",
+    "        assert len(times) > 100\n",
+    "    \n",
+    "        fig, ax = plt.subplots()\n",
+    "        ax.plot(times, I_dend)\n",
+    "        ax.set_ylabel(\"I_dend\")\n",
+    "        fig.savefig(\"/tmp/nestml_I_dend_\"  + str(gid) + \"_\" + fname_snip + \".png\")\n",
+    "\n",
+    "    events = model_instance.spike_recorder_inh_.get()[\"events\"]\n",
+    "    times = events[\"times\"]\n",
+    "    senders = events[\"senders\"]\n",
+    "\n",
+    "    fig, ax = plt.subplots()\n",
+    "\n",
+    "    for i, gid in enumerate(np.unique(senders)):\n",
+    "        idx = np.where(senders == gid)[0]\n",
+    "        spike_times = events[\"times\"][idx]\n",
+    "\n",
+    "        ax.scatter(spike_times, i * np.ones_like(spike_times), marker=\"D\", alpha=.5)\n",
+    "    ax.set_ylabel(\"Inhibitory neuron idx\")\n",
+    "    fig.savefig(\"/tmp/nestml_inhibitory_spikes_\" + str(gid) + \"_\" + fname_snip + \".png\")\n",
+    "        \n",
+    "    \n",
+    "    # print Ic\n",
+    "    #zs = np.array([nest.GetStatus(model_instance.exc_neurons)[i]['z'] for i in range(params['M']*params['n_E'])])\n",
+    "    #id_zs = np.where(zs>0.5)\n",
+    "    #print(zs[id_zs])\n",
+    "\n",
+    "    # load spikes from reference data\n",
+    "    somatic_spikes = load_spike_data('somatic_spikes')\n",
+    "    idend_eval = load_spike_data('idend_eval')\n",
+    "    excitation_times = load_data('excitation_times')\n",
+    "\n",
+    "    # get recoding times of dendriticAP\n",
+    "    idend_recording_times = load_data('idend_recording_times')\n",
+    "    characters_to_subpopulations = load_data('characters_to_subpopulations')\n",
+    "\n",
+    "    #seq_avg_errors, seq_avg_false_positives, seq_avg_false_negatives, _ = compute_prediction_performance(somatic_spikes, idend_eval, idend_recording_times, characters_to_subpopulations, model_instance.sequences, model_instance.params)\n",
+    "\n",
+    "    # get number of active neuron for each element in the sequence\n",
+    "    number_elements_per_batch = sum([len(seq) for seq in model_instance.sequences])\n",
+    "    start_time = excitation_times[-number_elements_per_batch] - 5 \n",
+    "    end_time = excitation_times[-1] + 5\n",
+    "\n",
+    "    idx_times = np.where((np.array(excitation_times) > start_time) & (np.array(excitation_times) < end_time))  \n",
+    "    excitation_times_sel = np.array(excitation_times)[idx_times]\n",
+    "\n",
+    "    num_active_neurons = number_active_neurons_per_element(model_instance.sequences, somatic_spikes[:,1], somatic_spikes[:,0], excitation_times_sel, params['fixed_somatic_delay'])\n",
+    "\n",
+    "    print(\"\\n##### testing sequences with number of somatic spikes \")\n",
+    "    count_false_negatives = 0\n",
+    "    for i, (sequence, seq_counts) in enumerate(zip(model_instance.sequences, num_active_neurons)): \n",
+    "        seq = ''\n",
+    "        for j, (char, counts) in enumerate(zip(sequence, seq_counts)):\n",
+    "            seq += str(char)+'('+ str(seq_counts[char])+')'.ljust(2)\n",
+    "\n",
+    "            if j != 0 and seq_counts[char] > 0.5*params['n_E']:\n",
+    "                count_false_negatives += 1\n",
+    "\n",
+    "        print(\"sequence %d: %s\" % (i, seq))   \n",
+    "\n",
+    "    print(\"False negative counts\", count_false_negatives)   \n",
+    "\n",
+    "    print(\"\\n### Plasticity parameters\")\n",
+    "    print(\"lambda plus: %0.4f\" % params['syn_dict_ee']['lambda_plus'])\n",
+    "    print(\"lambda homeostasis: %0.4f\" % params['syn_dict_ee']['lambda_h'])\n",
+    "    print(\"lambda minus: %0.4f\" % model_instance.params['syn_dict_ee']['lambda_minus']) \n",
+    "    #print(\"inh factor:\", params['inh_factor'])\n",
+    "    print(\"excitation step %0.1fms\" % params['DeltaT']) #30-50  \n",
+    "    print(\"seed number: %d\" % params['seed']) \n",
+    "    print(\"number of learning episodes: %d\" % params['learning_episodes'])\n",
+    "\n",
+    "    return model_instance\n",
+    "\n",
+    "params['store_connections'] = True    # N.B. this takes a very long time!\n",
+    "\n",
+    "model_instance = simulate_train_network(params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The initial sparse, random and immature network connectivity constitutes the skeleton on which the sequence-specific paths will be carved out during the learning process. Synaptic depression prunes connections not supporting the learned pattern, thereby reducing the chance of predicting wrong sequence items (false positives)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_connection_matrix_from_fname(p, label):\n",
+    "    print('Load connections from ' + label + '...')\n",
+    "    conns = np.load('%s.npy' % label)\n",
+    "    conns_tg = [int(conn[0]) for conn in conns]\n",
+    "    conns_src = [int(conn[1]) for conn in conns]\n",
+    "    conns_weights = [conn[2] for conn in conns]\n",
+    "\n",
+    "    min_gid = min(np.amin(conns_tg), np.amin(conns_src))\n",
+    "    max_gid = min(np.amax(conns_tg), np.amax(conns_src))\n",
+    "\n",
+    "    total_n_E = p['n_E'] * p['M']\n",
+    "    A = np.zeros((total_n_E, total_n_E))\n",
+    "    for gid_src, gid_tg, w in zip(conns_src, conns_tg, conns_weights):\n",
+    "        A[gid_src - min_gid, gid_tg - min_gid] = w\n",
+    "\n",
+    "    return A\n",
+    "\n",
+    "def get_connection_matrix_from_model_instance(model_instance):\n",
+    "    min_gid = np.amin(model_instance.exc_neurons.tolist())\n",
+    "    max_gid = np.amax(model_instance.exc_neurons.tolist())\n",
+    "\n",
+    "    total_n_E = len(model_instance.exc_neurons)\n",
+    "    A = np.zeros((total_n_E, total_n_E))\n",
+    "\n",
+    "    conns = nest.GetConnections(source=model_instance.exc_neurons,\n",
+    "                                target=model_instance.exc_neurons)\n",
+    "  \n",
+    "    sources = list(conns.sources())\n",
+    "    targets = list(conns.targets())\n",
+    "    if \"w\" in conns.get().keys():\n",
+    "        A[sources - np.amin(sources), targets - np.amin(targets)] = list(conns.get(\"w\"))\n",
+    "        \n",
+    "    elif \"weight\" in conns.get().keys():\n",
+    "        A[sources - np.amin(sources), targets - np.amin(targets)] = list(conns.get(\"weight\"))\n",
+    "\n",
+    "    return A\n",
+    "\n",
+    "def plot_connection_matrix_from_model_instance(model_instance, A, fname_snip=\"\"):\n",
+    "\n",
+    "    fig, ax = plt.subplots(figsize=(12, 12))\n",
+    "    ax.imshow(A)\n",
+    "    ax.set_ylabel(\"From neuron\")\n",
+    "    ax.set_xlabel(\"To neuron\")\n",
+    "\n",
+    "    for subax in [ax.xaxis, ax.yaxis]:\n",
+    "        subax.set_major_locator(mpl.ticker.MultipleLocator(model_instance.params[\"n_E\"]))\n",
+    "        subax.set_major_formatter(mpl.ticker.NullFormatter())\n",
+    "        subax.set_minor_locator(mpl.ticker.MultipleLocator(model_instance.params[\"n_E\"], offset=model_instance.params[\"n_E\"] / 2))\n",
+    "        subax.set_minor_formatter(mpl.ticker.FixedFormatter([\"\"] + model_instance.vocabulary))\n",
+    "\n",
+    "    fig.savefig(\"/tmp/W_model\"+fname_snip+\".png\",dpi=300)\n",
+    "\n",
+    "if params['store_connections']:\n",
+    "    A = get_connection_matrix_from_fname(params, \"ee_connections_before\")\n",
+    "    plot_connection_matrix_from_model_instance(model_instance, A, fname_snip=\"ee_connections_before\")\n",
+    "else:\n",
+    "    \"store_connections is False, skipping the plot\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To guarantee a successful learning, this initial skeleton must be neither too sparse nor too dense. Before learning, the presentation of a particular sequence element causes all neurons with the corresponding stimulus preference to reliably and synchronously fire a somatic action potential due to the strong, suprathreshold external stimulus. All other subpopulations remain silent. The lateral connectivity between excitatory neurons belonging to the different subpopulations is subject to a form of Hebbian structural plasticity. Repetitive and consistent sequential  presentation of sequence elements turns immature connections between successively activated subpopulations into mature connections, and hence leads to the formation of sequence-specific subnetworks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if params['store_connections']:\n",
+    "    A = get_connection_matrix_from_fname(params, \"ee_connections\")\n",
+    "    plot_connection_matrix_from_model_instance(model_instance, A, fname_snip=\"ee_connections\")\n",
+    "else:\n",
+    "    \"store_connections is False, skipping the plot\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "At the beginning of the learning process, all neurons of a stimulated subpopulation collectively fire in response to the external input. Non-stimulated neurons remain silent. As the connectivity is still immature at this point, no dAPs are triggered in postsynaptic neurons, and, hence, no predictions are generated. As a consequence, the prediction error, the false-negative rate and the number of active neurons (in stimulated populations) are at their maximum, and the false positive rate is zero."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def analyse_prediction_performance(params, model_instance, fname = 'prediction_performance'):\n",
+    "    PS = copy.deepcopy(params)\n",
+    "    PS_sel = copy.deepcopy(PS)\n",
+    "    compute_overlap = True\n",
+    "    \n",
+    "    PL = parameter_set_list(PS_sel)\n",
+    "    \n",
+    "    # get training data\n",
+    "    sequences = load_data('training_data')\n",
+    "    \n",
+    "    print(\"#### sequences used for training ### \")\n",
+    "    for i, sequence in enumerate(sequences): \n",
+    "        seq = '' \n",
+    "        for char in sequence:\n",
+    "            seq += str(char).ljust(2) \n",
+    "        print(\"sequence %d: %s\" % (i, seq))\n",
+    "    \n",
+    "    for cP, p in enumerate(PL):\n",
+    "    \n",
+    "        data = {}\n",
+    "    \n",
+    "        # get data path\n",
+    "        # load somatic spikes and dendritic current\n",
+    "        somatic_spikes = load_spike_data( 'somatic_spikes')\n",
+    "        idend_eval = load_spike_data('idend_eval')\n",
+    "    \n",
+    "        # load record and excitation times \n",
+    "        print(\"Loading idend_recording_times from \" + 'idend_recording_times')\n",
+    "        idend_recording_times = load_data('idend_recording_times')\n",
+    "        characters_to_subpopulations = load_data('characters_to_subpopulations')\n",
+    "        excitation_times = load_data('excitation_times')\n",
+    "    \n",
+    "        # compute prediction performance\n",
+    "        errors, false_positives, false_negatives, num_active_neurons = compute_prediction_performance(somatic_spikes, idend_eval, idend_recording_times, characters_to_subpopulations, sequences, p)\n",
+    "    \n",
+    "        if compute_overlap:\n",
+    "            # sequences overlap\n",
+    "            sequences_overlap = measure_sequences_overlap(sequences, somatic_spikes[:,1], somatic_spikes[:,0], excitation_times, p['fixed_somatic_delay'], p['learning_episodes'])\n",
+    "            data['overlap'] = sequences_overlap\n",
+    "    \n",
+    "        data['error'] = errors\n",
+    "        data['false_positive'] = false_positives\n",
+    "        data['false_negative'] = false_negatives\n",
+    "        data['rel_active_neurons'] = num_active_neurons/p['n_E']\n",
+    "        data['ep_num'] = p['episodes_to_testing'] * np.arange(int(p['learning_episodes']/p['episodes_to_testing'])+1)\n",
+    "    \n",
+    "        ep_to_sol = np.where(errors < 0.01)[0] \n",
+    "        if len(ep_to_sol) == 0:\n",
+    "            print(\"number of episodes to convergence\", p['learning_episodes'])\n",
+    "        else:    \n",
+    "            print(\"number of episodes to convergence\", data['ep_num'][ep_to_sol][0])\n",
+    "    \n",
+    "        # save data\n",
+    "        print(\"Saving to \" + fname)\n",
+    "        np.save(fname, data)\n",
+    "\n",
+    "    return data\n",
+    "\n",
+    "data = analyse_prediction_performance(params, model_instance)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_prediction_performance(fname = 'prediction_performance'):\n",
+    "\n",
+    "    data = np.load(\"prediction_performance.npy\", allow_pickle=True)\n",
+    "    data = data.item()\n",
+    "    \n",
+    "    \n",
+    "    fig, ax = plt.subplots(nrows=4)\n",
+    "    ax[0].plot(data[\"ep_num\"], data[\"error\"])\n",
+    "    ax[0].set_ylabel(\"error\")\n",
+    "\n",
+    "    ax[1].plot(data[\"ep_num\"], data[\"false_positive\"])\n",
+    "    ax[1].set_ylabel(\"fp\")\n",
+    "\n",
+    "    ax[2].plot(data[\"ep_num\"], data[\"false_negative\"])\n",
+    "    ax[2].set_ylabel(\"fn\")\n",
+    "\n",
+    "    ax[3].plot(data[\"ep_num\"], data[\"rel_active_neurons\"])\n",
+    "    ax[3].set_ylabel(\"activity\")\n",
+    "\n",
+    "    ax[-1].set_xlabel(\"Training episode\")\n",
+    "    plt.savefig(fname)\n",
+    "\n",
+    "plot_prediction_performance(fname='/tmp/prediction_performance.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "During the first training episodes, the consistent collective firing of subsequently activated populations leads to the formation of mature connections as a result of the Hebbian structural plasticity. Upon reaching of a critical\n",
+    "number of mature synapse, first dAPs (predictions) are generated in postsynaptic cells.\n",
+    "\n",
+    "As a consequence, the false negative rate decreases, and the stimulus responses become more sparse. At this early phase of the learning, the predictions of upcoming sequence elements are not yet context-specific (for sequence set I, non-sparse activity in “B” triggers a prediction in both “E” and “C”, irrespective of the context). Hence, the false-positive rate transiently increases. As the context specific connectivity is not consolidated at this point, more and more presynaptic subpopulations fail at triggering dAPs in their postsynaptic targets when they switch to sparse firing. Therefore, the false-positive rate decreases again, and the false-negative rate increases. In other words, there exists a negative feedback loop in the interim learning dynamics where the generation of predictions leads to an increase in sparsity which, in turn, causes prediction failures (and, hence, non-sparse firing). With an increasing number of training episodes, synaptic depression and homeostatic regulation increase context selectivity and thereby break this loop. Eventually, sparse firing of presynaptic populations is sufficient to reliably trigger\n",
+    "predictions in their postsynaptic targets. \n",
+    "\n",
+    "During the learning process, the number of mature connections grows to a point where the activation of a certain subpopulation by an external input generates dendritic action potentials (dAPs), a \"prediction\", in a subset of neurons in the subsequent subpopulation.\n",
+    "\n",
+    "If the number of predictive neurons within a subpopulation is sufficiently large, their advanced spikes initiate a fast and strong inhibitory feedback to the entire subpopulation, and thereby suppress subsequent firing of non-predictive neurons in this population. Owing to this winner-take-all dynamics, the network generates sparse spiking in response to predicted stimuli, i.e., if the external input coincides with a dAP-triggered somatic depolarization. In the presence of a non-anticipated, non-predicted stimulus, the neurons in the corresponding subpopulation fire collectively in a non-sparse manner, thereby signaling a “mismatch”."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_network_dynamics(data, params, xmax=None, fname_snip=\"\"):\n",
+    "\n",
+    "    # get parameters \n",
+    "    PS = copy.deepcopy(params)\n",
+    "    PS_sel = copy.deepcopy(PS)\n",
+    "    params = PS_sel\n",
+    "    num_neurons = params['M'] * params['n_E']\n",
+    "    \n",
+    "    print('#### sequences used for training ### ')\n",
+    "    for i, sequence in enumerate(data[\"sequences\"]):\n",
+    "        seq = ''\n",
+    "        for char in sequence:\n",
+    "            seq += str(char).ljust(2)\n",
+    "        print('sequence %d: %s' % (i, seq))\n",
+    "    \n",
+    "    # get dendritic AP\n",
+    "    idx = np.where((data[\"idend\"][:, 2] > params['soma_params']['theta_dAP']))[0]\n",
+    "    dendriticAP_currents = data[\"idend\"][:, 2][idx]\n",
+    "    dendriticAP_times = data[\"idend\"][:, 1][idx]\n",
+    "    dendriticAP_senders = data[\"idend\"][:, 0][idx]\n",
+    "    \n",
+    "    # organize the characters for plotting purpose\n",
+    "    subpopulation_indices = []\n",
+    "    chars_per_subpopulation = []\n",
+    "    for char in data[\"vocabulary\"]:\n",
+    "        # shift the subpopulation indices for plotting purposes \n",
+    "        char_to_subpopulation_indices = data[\"characters_to_subpopulations\"][char]\n",
+    "        subpopulation_indices.extend(char_to_subpopulation_indices)\n",
+    "    \n",
+    "        chars_per_subpopulation.extend(char * len(data[\"characters_to_subpopulations\"][char]))\n",
+    "    \n",
+    "    shifted_subpopulation_indices = np.array(subpopulation_indices) + 0.5\n",
+    "    \n",
+    "    panel_label_pos = (-0.14,0.5)\n",
+    "    panel_labels = ['B', 'D', 'F']\n",
+    "    color_soma_spike = '#DB2763'\n",
+    "    color_dendrite_spike = '#00B4BE' \n",
+    "    fc_bg = '#dcdcdc'\n",
+    "    fraction_active = 3\n",
+    "    delta_time = 5.\n",
+    "    ymin = -0.1\n",
+    "    ymax = 2\n",
+    "    xmin = 0\n",
+    "    master_file_name = 'replay_network_activity'\n",
+    "    \n",
+    "    start_time = 0.\n",
+    "    end_time = start_time + 150.\n",
+    "    \n",
+    "    if not xmax is None:\n",
+    "        end_time = max(end_time, xmax)\n",
+    "        \n",
+    "    # postprocess somatic spikes\n",
+    "    somatic_spikes_times = data[\"somatic_spikes\"][:,1]#[idx_somatic_spikes]\n",
+    "    somatic_spikes_senders = data[\"somatic_spikes\"][:,0]#[idx_somatic_spikes]\n",
+    "    initial_time = 0.#somatic_spikes_times[0]\n",
+    "    #somatic_spikes_times -= initial_time\n",
+    "    if xmax is None:\n",
+    "        xmax = somatic_spikes_times[-1] + delta_time\n",
+    "\n",
+    "    # postporcess dendritic AP\n",
+    "    dAP_senders = dendriticAP_senders#[idx_dAP]\n",
+    "    dAP_currents = dendriticAP_currents#[idx_dAP]\n",
+    "    dAP_times = dendriticAP_times#[idx_dAP]\n",
+    "    #dAP_times -= initial_time\n",
+    "\n",
+    "    idx_exc_times = np.where((data[\"excitation_times\"] > start_time) & (data[\"excitation_times\"] < end_time))\n",
+    "    excitation_times_sel = data[\"excitation_times\"][idx_exc_times]\n",
+    "\n",
+    "    \n",
+    "    # set up the figure frame\n",
+    "    fig, ax = plt.subplots(figsize=(12,4))\n",
+    "\n",
+    "    # SOMA SPIKES\n",
+    "    senders_subsampled = somatic_spikes_senders\n",
+    "    line1 = ax.plot(somatic_spikes_times, somatic_spikes_senders, 'o', color=color_soma_spike, lw=0., ms=0.5, zorder=2)\n",
+    "\n",
+    "    # DENDRITIC SPIKES\n",
+    "    for xx, sender in enumerate(senders_subsampled):\n",
+    "        idx_sub = np.where(dAP_senders == sender)\n",
+    "        line2 = plt.plot(dAP_times[idx_sub], dAP_senders[idx_sub], 'o', markersize=.5, color=color_dendrite_spike, zorder=1)\n",
+    "        #line2 = plt.scatter(dAP_times[idx_sub], dAP_senders[idx_sub], color=color_dendrite_spike, s=.5, zorder=1)\n",
+    "\n",
+    "    for char in data[\"characters_to_time_excitation\"].keys():\n",
+    "        char_stim_times = data[\"characters_to_time_excitation\"][char]\n",
+    "        for t in char_stim_times:\n",
+    "            ax.text(t - 0.003, 10, char, horizontalalignment='center')\n",
+    "            arrow = mpl.patches.FancyArrowPatch(posA=(t, 0.), posB=(t, 10), arrowstyle='->', color='green', mutation_scale=10.)\n",
+    "            ax.add_patch(arrow)\n",
+    "\n",
+    "    ax.set_xlim(-delta_time, xmax)\n",
+    "    ax.set_ylim(-10, num_neurons+10)\n",
+    "\n",
+    "    ticks_pos = shifted_subpopulation_indices * params['n_E']\n",
+    "    ticks_label = chars_per_subpopulation\n",
+    "    subpopulation_indices_background = np.arange(params['M'])*params['n_E']\n",
+    "\n",
+    "    ax.set_yticks(ticks_pos, ticks_label)\n",
+    "    ax.tick_params(labelbottom=False)\n",
+    "\n",
+    "    for i in range(params['M'])[::2]:\n",
+    "        ax.axhspan(subpopulation_indices_background[i], subpopulation_indices_background[i]+params['n_E'], facecolor=fc_bg, zorder=0)\n",
+    "\n",
+    "    ax.set_xlabel('time (ms)')\n",
+    "    ax.tick_params(labelbottom=True)\n",
+    "\n",
+    "    fname = '/tmp/' + master_file_name + fname_snip + '.png'\n",
+    "    print(\"Saving figure to \" + fname)\n",
+    "    fig.tight_layout()\n",
+    "\n",
+    "    plt.savefig(fname, dpi=300)\n",
+    "    plt.close(fig)\n",
+    "\n",
+    "\n",
+    "def load_data_from_files():\n",
+    "    data = {}\n",
+    "\n",
+    "    # get trained sequences and vocabulary\n",
+    "    data[\"sequences\"] = load_data('training_data')\n",
+    "    data[\"vocabulary\"] = load_data('vocabulary')\n",
+    "\n",
+    "    # load spikes\n",
+    "    data[\"somatic_spikes\"] = load_spike_data('somatic_spikes')\n",
+    "    data[\"idend\"] = load_spike_data('idend_last_episode')\n",
+    "\n",
+    "    # load spike recordings\n",
+    "    data[\"idend_recording_times\"] = load_data('idend_recording_times')\n",
+    "    data[\"characters_to_subpopulations\"] = load_data('characters_to_subpopulations')\n",
+    "    data[\"characters_to_time_excitation\"] = load_data('excitation_times_soma')\n",
+    "\n",
+    "    # load excitation times\n",
+    "    data[\"excitation_times\"] = load_data('excitation_times')\n",
+    "\n",
+    "    return data\n",
+    "\n",
+    "data = load_data_from_files()\n",
+    "plot_network_dynamics(data, params, fname_snip=\"_during_training\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = get_connection_matrix_from_model_instance(model_instance)\n",
+    "plot_connection_matrix_from_model_instance(model_instance, A, fname_snip=\"after_training\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Experiment 4: Autonomous replay of sequences\n",
+    "\n",
+    "We can cue the network to recall an entire sequence by presenting only the first item in the sequence. To set up the network into this autonomous replay mode, the somatic firing threshold voltage, as well as the dAP threshold voltage are lowered."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params = create_sequence_learning_parameters()\n",
+    "DELAY = 0.1\n",
+    "params['record_idend_last_episode'] = True\n",
+    "params['syn_dict_ei']['delay'] = 2*DELAY"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_replay_experiment(PS):\n",
+    "    PS = copy.deepcopy(PS)\n",
+    "    PL = parameter_set_list(PS) \n",
+    " \n",
+    "    array_id = 0\n",
+    "\n",
+    "    params = PL[array_id]\n",
+    "    params['learning_episodes'] = 4 \n",
+    "    params['episodes_to_testing'] = 1\n",
+    "    params['load_connections'] = True\n",
+    "    params['evaluate_replay'] = True\n",
+    "    params['evaluate_performance'] = False\n",
+    "    params['store_training_data'] = False\n",
+    "    params['DeltaT_cue'] = 250.\n",
+    "    \n",
+    "    \n",
+    "    # record dendritic current for only DeltaT=40.\n",
+    "    # record for the sequence replay plot\n",
+    "    if params['DeltaT'] == 40.:\n",
+    "        params['record_idend_last_episode'] = True\n",
+    "\n",
+    "    params['soma_params']['V_th'] *= 0.25\n",
+    "    # imporant: the adjustment of theta_dAP is only important for large interstimulus intervals (above 75ms)\n",
+    "    # As the potentiation is small for large (\\DeltaT), during the predictive mode the total PSC is barely above the dAP.\n",
+    "    # During the replay mode, the dispersion in the firing times causes the postsynaptic neurons to not have sufficient PSC to generate dAPs.\n",
+    "    # Adjusting theta_dAP addresses this issue\n",
+    "    # there might be other solutions such as increasing the number of learning episodes or adjusting the learning rates\n",
+    "    params['soma_params']['theta_dAP'] *= 0.7\n",
+    "\n",
+    "    # disable learning\n",
+    "    params['syn_dict_ee']['synapse_model'] = 'static_synapse'     # synapse model\n",
+    "    \n",
+    "    params['idend_recording_interval'] = params['dt']\n",
+    "\n",
+    "    # start time \n",
+    "    time_start = time.time()\n",
+    "\n",
+    "    # ###############################################################\n",
+    "    # create network\n",
+    "    # ===============================================================\n",
+    "    model_instance = Model(params, sequences, vocabulary)\n",
+    "    time_model = time.time()\n",
+    "\n",
+    "    model_instance.create()\n",
+    "    time_create = time.time()\n",
+    "\n",
+    "    # ###############################################################\n",
+    "    # connect the netwok\n",
+    "    # ===============================================================\n",
+    "    model_instance.connect()\n",
+    "    time_connect = time.time()\n",
+    "\n",
+    "    # ###############################################################\n",
+    "    # train the network\n",
+    "    # ===============================================================\n",
+    "    # simulate network     \n",
+    "    print(\"Running replay experiment...\")\n",
+    "    clear_recorded_data()\n",
+    "    model_instance.simulate()\n",
+    "    time_simulate = time.time()\n",
+    "    \n",
+    "    A = get_connection_matrix_from_model_instance(model_instance)\n",
+    "    plot_connection_matrix_from_model_instance(model_instance, A, fname_snip=\"after_replay\")\n",
+    "\n",
+    "    print(\n",
+    "        '\\nTimes of Rank {}:\\n'.format(\n",
+    "            nest.Rank()) +\n",
+    "        '  Total time:          {:.3f} s\\n'.format(\n",
+    "            time_simulate -\n",
+    "            time_start) +\n",
+    "        '  Time to initialize:  {:.3f} s\\n'.format(\n",
+    "            time_model -\n",
+    "            time_start) +\n",
+    "        '  Time to create:      {:.3f} s\\n'.format(\n",
+    "            time_create -\n",
+    "            time_model) +\n",
+    "        '  Time to connect:     {:.3f} s\\n'.format(\n",
+    "            time_connect -\n",
+    "            time_create) +\n",
+    "        '  Time to simulate:    {:.3f} s\\n'.format(\n",
+    "            time_simulate -\n",
+    "            time_connect))\n",
+    "\n",
+    "run_replay_experiment(params)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When exposing a network that has learned the two\n",
+    "sequences {A,D,B,E} and {F,D,B,C} to the elements “A” and “F”, different subsets of\n",
+    "neurons are activated in “D” and “B”. By virtue of these sequence specific activation\n",
+    "patterns, stimulation by {A,D,B} or {F,D,B} leads to correct predictions “E” or “C”,\n",
+    "respectively"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = load_data_from_files()\n",
+    "plot_network_dynamics(data, params, fname_snip=\"replay\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def replay_analysis(PS):\n",
+    "    ##################################################################\n",
+    "    # function to return key for any value \n",
+    "    def get_key(my_dict, val): \n",
+    "        for key, value in my_dict.items(): \n",
+    "            if val == value: \n",
+    "                return key \n",
+    "    \n",
+    "        return \"key doesn't exist\"\n",
+    "\n",
+    "    PS = copy.deepcopy(PS)\n",
+    "    \n",
+    "    # parameters list\n",
+    "    PL = parameter_set_list(PS) \n",
+    "    \n",
+    "    #TODO: use argparse with default values\n",
+    "    try: \n",
+    "        batch_id=int(sys.argv[1])\n",
+    "        batch_array_id=int(sys.argv[2])\n",
+    "        JOBMAX=int(sys.argv[3])\n",
+    "        array_id=batch_id*JOBMAX+batch_array_id\n",
+    "    except:\n",
+    "        array_id = 0\n",
+    "    \n",
+    "    params = PL[array_id]\n",
+    "    \n",
+    "    # get parameters \n",
+    "    #PS, PS_path = get_parameter_set(path_dict)\n",
+    "    num_neurons = params['M'] * params['n_E']\n",
+    "    num_trials = 10\n",
+    "    \n",
+    "    # get trained sequences\n",
+    "    sequences = load_data( 'training_data')\n",
+    "    len_seqs = len(sequences)\n",
+    "    print(\"len_seqs = \" + str(len_seqs))\n",
+    "    \n",
+    "    # load spiking data\n",
+    "    somatic_spikes = load_spike_data('somatic_spikes')\n",
+    "    \n",
+    "    # get excitation times \n",
+    "    characters_to_subpopulations = load_data('characters_to_subpopulations')\n",
+    "    excitation_times = load_data('excitation_times')\n",
+    "    \n",
+    "    #################################\n",
+    "    # Postprocess data\n",
+    "    # -------------------------------\n",
+    "    \n",
+    "    sequences_active_neurons = [[] for _ in range(len_seqs)]\n",
+    "    sequences_firing_times = [[] for _ in range(len_seqs)]\n",
+    "    replay_durations = []\n",
+    "    #for num_trials in range(params['learning_episodes']-2):\n",
+    "    for id_trial in range(num_trials-1): \n",
+    "    \n",
+    "        firing_times = defaultdict(list)\n",
+    "        active_neurons = defaultdict(list)\n",
+    "    \n",
+    "        for k, seq in enumerate(sequences):\n",
+    "    \n",
+    "            # select data for first sequence\n",
+    "            start_time = excitation_times[id_trial*len_seqs+k] \n",
+    "            end_time = excitation_times[id_trial*len_seqs+1+k]\n",
+    "    \n",
+    "            # select somatic spikes to process\n",
+    "            ind_somatic = np.where((somatic_spikes[:,1] > start_time) & (somatic_spikes[:,1] < end_time))\n",
+    "    \n",
+    "            times_somatic_spikes = somatic_spikes[:,1][ind_somatic]\n",
+    "            senders_somatic_spikes = somatic_spikes[:,0][ind_somatic]\n",
+    "            initial_time = times_somatic_spikes[0]\n",
+    "            times_somatic_spikes -= initial_time\n",
+    "            #xmax = times_somatic_spikes[-1] + pad_time\n",
+    "    \n",
+    "            for index_sender in senders_somatic_spikes:\n",
+    "    \n",
+    "                num_subpopulation = int((index_sender-1) / params['n_E'])\n",
+    "    \n",
+    "                index_time = np.where(senders_somatic_spikes == index_sender)\n",
+    "                firing_times[get_key(characters_to_subpopulations, num_subpopulation)].append(times_somatic_spikes[index_time][0])\n",
+    "                active_neurons[get_key(characters_to_subpopulations, num_subpopulation)].append(index_sender)\n",
+    "    \n",
+    "        first_letter_mean_firing_times = np.mean(firing_times[sequences[0][0]])\n",
+    "        last_letter_mean_firing_times = np.mean(firing_times[sequences[0][-1]])\n",
+    "    \n",
+    "        replay_duration = last_letter_mean_firing_times - first_letter_mean_firing_times\n",
+    "        replay_durations.append(replay_duration)\n",
+    "    \n",
+    "    data = {}\n",
+    "    data['mean_replay_duration'] = np.mean(replay_durations)\n",
+    "    data['std_replay_duration'] = np.std(replay_durations)\n",
+    "    print('mean replay duration:', data['mean_replay_duration'])\n",
+    "    print('std replay duration:', data['std_replay_duration'])\n",
+    "    \n",
+    "    print('--------------------------------------------------')\n",
+    "    fname = 'replay_duration' \n",
+    "    np.save(fname, data)\n",
+    "\n",
+    "#replay_analysis(params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Conclusions/further steps\n",
+    "-------------------------\n",
+    "\n",
+    "- Playback speed experiments: dAPs equip neurons with a third type of state (next to the quiescent and the firing\n",
+    "    state): the predictive state, i.e., a long lasting (∼50-200 ms ) strong depolarization of\n",
+    "    the soma. Due to the prolonged depolarization of the soma, the inter-stimulus interval\n",
+    "    can be much larger than the synaptic time constants and delays.\n",
+    "    \n",
+    "    Try to reproduce Fig. 10 from [2].\n",
+    "\n",
+    "Acknowledgements\n",
+    "------------------\n",
+    "\n",
+    "Many thanks to Younes Bouhadjar for his work and fruitful discussions on the sequence learning network.\n",
+    "\n",
+    "This software was developed in part or in whole in the Human Brain Project, funded from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under Specific Grant Agreements No. 720270 and No. 785907 (Human Brain Project SGA1 and SGA2).\n",
+    "\n",
+    "\n",
+    "References\n",
+    "----------\n",
+    "\n",
+    "[1] Younes Bouhadjar. A brain inspired sequence learning algorithm and foundations of a memristive hardware implementation Younes Bouhadjar (PhD thesis, RWTH Aachen). https://doi.org/10.18154/RWTH-2023-03627\n",
+    "\n",
+    "[2] Bouhadjar Y, Wouters DJ, Diesmann M, Tetzlaff T (2022) Sequence learning, prediction, and replay in networks of spiking neurons. PLoS Comput Biol 18(6): e1010233. https://doi.org/10.1371/journal.pcbi.1010233\n",
+    "\n",
+    "\n",
+    "Copyright\n",
+    "------------\n",
+    "\n",
+    "This file is part of NEST.\n",
+    "\n",
+    "Copyright (C) 2004 The NEST Initiative\n",
+    "\n",
+    "NEST is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 2 of the License, or (at your option) any later version.\n",
+    "\n",
+    "NEST is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.\n",
+    "\n",
+    "You should have received a copy of the GNU General Public License along with NEST. If not, see http://www.gnu.org/licenses/.\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/doc/tutorials/tutorials_list.rst b/doc/tutorials/tutorials_list.rst
index 201749322..139eb240d 100644
--- a/doc/tutorials/tutorials_list.rst
+++ b/doc/tutorials/tutorials_list.rst
@@ -21,6 +21,10 @@ Creating neuron models
 
   Create a model that emits spikes according to an inhomogeneous Poisson distribution.
 
+* :doc:`Sequence learning network </tutorials/sequence_learning/sequence_learning>`
+
+  A network learns to predict and autonomously replay sequences of items.
+
 
 Creating synapse models
 -----------------------
diff --git a/models/synapses/stdsp_synapse.nestml b/models/synapses/stdsp_synapse.nestml
new file mode 100644
index 000000000..9eff003c1
--- /dev/null
+++ b/models/synapses/stdsp_synapse.nestml
@@ -0,0 +1,82 @@
+"""
+stdsp_synapse - Synapse model for spike-timing dependent plasticity with postsynaptic third-factor modulation
+#############################################################################################################
+
+Description
++++++++++++
+
+References
+++++++++++
+
+"""
+model stdsp_synapse:
+    state:
+        permanence real = 1.
+        t_last_pre_spike ms = 0 ms
+        pre_trace real = 0.
+        w real = 100.    # dummy synaptic weight
+
+    parameters:
+        d ms = 1 ms    # Synaptic transmission delay
+
+        tau_pre_trace ms = 80 ms
+        lambda_h real = 1.
+        zt pA = 1 pA
+        lambda_plus real = .01
+        lambda_minus real = 1.
+        Wmax real = 100.
+        permanence_max real = 100.
+        permanence_min real = 0.
+        dt_min ms = 4 ms
+        dt_max ms = 100 ms
+
+        permanence_threshold real = 10.
+
+        Wmin real = 0.
+
+    equations:
+        pre_trace' = -pre_trace / tau_pre_trace
+
+    input:
+        pre_spikes <- spike
+        post_spikes <- spike
+        dAP_trace pA <- continuous
+
+    output:
+        spike
+
+    onReceive(post_spikes):
+        time_since_last_spike ms = t - t_last_pre_spike
+
+        if time_since_last_spike < dt_max and time_since_last_spike > dt_min:
+            # facilitation
+            norm_perm real = permanence / permanence_max + lambda_plus * pre_trace
+            permanence = min(norm_perm * permanence_max, permanence_max)
+
+            # homeostasis
+            permanence += lambda_h * (zt - dAP_trace) / pA * permanence_max
+            permanence = min(permanence, permanence_max)
+            permanence = max(permanence, permanence_min)
+
+    onReceive(pre_spikes):
+        t_last_pre_spike = t
+
+        pre_trace += 1.
+
+        # depress synapse
+        permanence -= lambda_minus * permanence_max
+        permanence = max(permanence, permanence_min)
+
+        if permanence > permanence_threshold:
+            # set a dummy "weight" so the weight can be recorded
+            w = Wmax
+
+            # deliver spike to postsynaptic partner
+            emit_spike(w, d)
+        else:
+            # set a dummy "weight" so the weight can be recorded
+            w = 0.
+
+    update:
+        # solve ODEs
+        integrate_odes()