BindsNET_Neuron_Demo.ipynb

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    "# BindsNET Neuron (Node) Models\n",
    "\n",
    "## 1. Table of Contents\n",
    "1. Table of Contents\n",
    "2. Overview\n",
    "3. Import Statements\n",
    "4. Neuron Models\n",
    "    1. Integrate-and-Fire (IF)\n",
    "    2. Leaky Integrate-and-Fire (LIF)\n",
    "    3. Spike Response Model 0 (SRM0)\n",
    "5. Spike Traces"
   ],
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    "## 2. Overview\n",
    "Detailed documentation of the node creation process has been specified [here](https://bindsnet-docs.readthedocs.io/guide/guide_part_i.html#creating-a-network). This document will go into more specific examples of configuring a spiking neural network in BindsNET."
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   "source": [
    "## 3. Import Statements"
   ],
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   "source": [
    "import os\n",
    "import torch\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from bindsnet.encoding import *\n",
    "from bindsnet.network import Network\n",
    "from bindsnet.network.nodes import Input, LIFNodes, IFNodes, SRM0Nodes, AdaptiveLIFNodes\n",
    "from bindsnet.network.topology import Connection\n",
    "from bindsnet.network.monitors import Monitor\n",
    "from bindsnet.network.monitors import NetworkMonitor\n",
    "\n",
    "from bindsnet.analysis.plotting import plot_spikes, plot_voltages, plot_input, plot_weights"
   ]
  },
  {
   "source": [
    "## 4. Neuron Models"
   ],
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    "### 4a. Integrate-and-Fire (IF) Model\n",
    "\n",
    "Incoming spikes are integrated (added) to the membrane potential.\n",
    "\n",
    "This model was adapted from http://neuronaldynamics.epfl.ch/online/Ch1.S3.html\n",
    "\n",
    "### Parameters\n",
    "Below is a table that describes the parameters that can be tweaked to customize the IF Neuron model.\n",
    "\n",
    "| Parameter       | Type                      | Description                                   | Default Value |\n",
    "|-----------------|---------------------------|-----------------------------------------------|---------------|\n",
    "| n               | Optional\\[int]             | The number of neurons in the layer.           | None          |\n",
    "| shape           | Optional\\[Iterable\\[int]]   | The dimensionality of the layer.              | None          |\n",
    "| traces          | bool                      | Whether to record spike traces.               | False         |\n",
    "| traces_additive | bool                      | Whether to record spike traces additively.    | False         |\n",
    "| tc_trace        | Union\\[float torch.Tensor] | Time constant of spike trace decay.           | 20.0          |\n",
    "| trace_scale     | Union\\[float torch.Tensor] | Scaling factor for spike trace.               | 1.0           |\n",
    "| sum_input       | bool                      | Whether to sum all inputs.                    | False         |\n",
    "| thresh          | Union\\[float torch.Tensor] | Spike threshold voltage.                      | -52.0         |\n",
    "| reset           | Union\\[float torch.Tensor] | Post-spike reset voltage.                     | -65.0         |\n",
    "| refrac          | Union\\[int torch.Tensor]   | Refractory (non-firing) period of the neuron. | 5             |\n",
    "| lbound          | float                     | Lower bound of the voltage.                   | None          |\n",
    "\n",
    "---------------------\n",
    "\n",
    "### 4b. Leaky Integrate-and-Fire (LIF) Model\n",
    "\n",
    "Incoming spikes are integrated (added) to the membrane potential. The membrane potential decrements at each timestep.\n",
    "\n",
    "This model was adapted from http://icwww.epfl.ch/~gerstner/SPNM/node26.html#SECTION02311000000000000000\n",
    "\n",
    "### Parameters\n",
    "Below is a table that describes the parameters that can be tweaked to customize the LIF Neuron model.\n",
    "\n",
    "| Parameter       | Type                       | Description                                   | Default Value |\n",
    "|-----------------|----------------------------|-----------------------------------------------|---------------|\n",
    "| n               | Optional\\[int]              | The number of neurons in the layer.           | None          |\n",
    "| shape           | Optional\\[Iterable\\[int]]    | The dimensionality of the layer.              | None          |\n",
    "| traces          | bool                       | Whether to record spike traces.               | False         |\n",
    "| traces_additive | bool                       | Whether to record spike traces additively.    | False         |\n",
    "| tc_trace        | Union\\[float, torch.Tensor] | Time constant of spike trace decay.           | 20.0          |\n",
    "| trace_scale     | Union\\[float, torch.Tensor] | Scaling factor for spike trace.               | 1.0           |\n",
    "| sum_input       | bool                       | Whether to sum all inputs.                    | False         |\n",
    "| thresh          | Union\\[float, torch.Tensor] | Spike threshold voltage.                      | -52.0         |\n",
    "| rest            | Union\\[float, torch.Tensor] | Resting membrane voltage.                     | -65.0         |\n",
    "| reset           | Union\\[float, torch.Tensor] | Post-spike reset voltage.                     | -65.0         |\n",
    "| refrac          | Union\\[int, torch.Tensor]   | Refractory (non-firing) period of the neuron. | 5             |\n",
    "| tc_decay        | Union\\[float, torch.Tensor] | Time constant of neuron voltage decay.        | 100.0         |\n",
    "| lbound          | float                      | Lower bound of the voltage.                   | None          |\n",
    "\n",
    "---------------------\n",
    "\n",
    "### 4c. Adaptive Leaky Integrate-and-Fire (LIF) Model\n",
    "\n",
    "Incoming spikes are integrated (added) to the membrane potential. The membrane potential decrements at each timestep. The voltage threshold is increased by some constant each time it spikes. Otherwise, it decays back to its default value.\n",
    "\n",
    "### Parameters\n",
    "Below is a table that describes the parameters that can be tweaked to customize the LIF Neuron model.\n",
    "\n",
    "| Parameters      | Type            | Description                                   | Default Value |\n",
    "|-----------------|-----------------|-----------------------------------------------|---------------|\n",
    "| n               | n               | The number of neurons in the layer.           | None          |\n",
    "| shape           | shape           | The dimensionality of the layer.              | None          |\n",
    "| traces          | traces          | Whether to record spike traces.               | False         |\n",
    "| traces_additive | traces_additive | Whether to record spike traces additively.    | False         |\n",
    "| tc_trace        | tc_trace        | Time constant of spike trace decay.           | 20.0          |\n",
    "| trace_scale     | trace_scale     | Scaling factor for spike trace.               | 1.0           |\n",
    "| sum_input       | sum_input       | Whether to sum all inputs.                    | False         |\n",
    "| rest            | rest            | Resting membrane voltage.                     | -65.0         |\n",
    "| reset           | reset           | Post-spike reset voltage.                     | -65.0         |\n",
    "| thresh          | thresh          | Spike threshold voltage.                      | -52.0         |\n",
    "| refrac          | refrac          | Refractory (non-firing) period of the neuron. | 5             |\n",
    "| tc_decay        | tc_decay        | Time constant of neuron voltage decay.        | 100.0         |\n",
    "| theta_plus      | theta_plus      | Voltage increase of threshold after spiking.  | 0.05          |\n",
    "| tc_theta_decay  | tc_theta_decay  | Time constant of adaptive threshold decay.    | 1e7           |\n",
    "| lbound          | lbound          | Lower bound of the voltage.                   | None          |\n",
    "\n",
    "---------------------\n",
    "\n",
    "### 4d. Spike Response Model 0 (SRM0)\n",
    "\n",
    "Incoming spikes are integrated (added) to the membrane potential. The membrane potential decrements at each timestep.\n",
    "\n",
    "This model was adapted from https://intranet.physio.unibe.ch/Publikationen/Dokumente/Vasilaki2009PloSComputBio_1.pdf\n",
    "\n",
    "### Parameters\n",
    "Below is a table that describes the parameters that can be tweaked to customize the SRM0 Neuron model.\n",
    "\n",
    "| Parameter       | Type                       | Description                                          | Default Value |\n",
    "|-----------------|----------------------------|------------------------------------------------------|---------------|\n",
    "| n               | Optional\\[int]              | The number of neurons in the layer.                  | None          |\n",
    "| shape           | Optional\\[Iterable[int]]    | The dimensionality of the layer.                     | None          |\n",
    "| traces          | bool                       | Whether to record spike traces.                      | False         |\n",
    "| traces_additive | bool                       | Whether to record spike traces additively.           | False         |\n",
    "| tc_trace        | Union\\[float, torch.Tensor] | Time constant of spike trace decay.                  | 20.0          |\n",
    "| trace_scale     | Union\\[float, torch.Tensor] | Scaling factor for spike trace.                      | 1.0           |\n",
    "| sum_input       | bool                       | Whether to sum all inputs.                           | False         |\n",
    "| thresh          | Union\\[float, torch.Tensor] | Spike threshold voltage.                             | -50.0         |\n",
    "| rest            | Union\\[float, torch.Tensor] | Resting membrane voltage.                            | -70.0         |\n",
    "| reset           | Union\\[float, torch.Tensor] | Post-spike reset voltage.                            | -70.0         |\n",
    "| refrac          | Union\\[int, torch.Tensor]   | Refractory (non-firing) period of the neuron.        | 5             |\n",
    "| tc_decay        | Union\\[float, torch.Tensor] | Time constant of neuron voltage decay.               | 10.0          |\n",
    "| lbound          | float                      | Lower bound of the voltage.                          | None          |\n",
    "| eps_0           | Union\\[float, torch.Tensor] | Scaling factor for pre-synaptic spike contributions. | 1.0           |\n",
    "| rho_0           | Union\\[float, torch.Tensor] | Stochastic intensity at threshold.                   | 1.0           |\n",
    "| d_thresh        | Union\\[float, torch.Tensor] | Width of the threshold region.                       | 5.0           |\n",
    "\n",
    "<!--\n",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 576x324 with 1 Axes>",
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n5EQVVUVFhZsxY0bQYYiIiEiGPfPMM3HnXO+R6nMjwZkxYwYbN24MOgwRERHJMDPrPeo1oCoqERERyUFKcERERCTnKMERERGRnKMER0RERHKOEhwRERHJOYEkOGZ2l5ntM7Pn/dur/eU3m9kzZvai//eGIOITERGRkS3IbuLfcc59s9eyOPBa59x+M7sIb+bjKZkPTUREREayrBoHxzn3XMrdzcAoMyt0zrUHFZOIiIiMPEG2wfmAmW0ys3vMbFwf698EPNtfcmNmd5jZRjPbGIvF0hupiIiIjChpS3DM7FEze6mP263AD4BZwELgAPCtXo+9EPga8Hf9Pb9z7m7n3CLn3KLKytNGaBaRHPbwiwf4zP++SH1Dc9ChiEiWSlsVlXPupqFsZ2Y/Ah5KuT8V+F/gnc65bWkKT0RGsO89VkfNweP84und/MXFk/jgDXOYN7E06LBEJIsE0gbHzCY55w74d98AvOQvLwN+D3zKObcuiNhEJLvtPdJKzcHjvG/ZLAB+tn4nD206wCsvnMgHb5zNhZPHBhyhiGSDoBoZf93MFgIO2MnJqqgPALOBz5nZ5/xltzjnGjIeoYhkpRU13ungtsunMquyhDuWVvOTdTv4ybqd/HHzQW46v4oP3DCHhdPKgg1URAJlzrmgYzhnixYtcppNXCQ//PVPnmZ7vIWVH1+GmfUsP3qik5+u38k963bQ1NrJ0jkVfOjGOVwxozzAaEUk3czsGefcot7LNZKxiIwYrR1drNvWyA3zq05JbgDGjoryoRvnsPaTN/CpV83n5QPHePO/P8GmvU3BBCsigVKCIyIjxvr6Rjq6Etw4f0K/25QURrjz+ln88B3eBV1jc0emwhORLKIER0RGjMdqGiguCHPlzMGrnQoj3umtszuR7rBEJAspwRGREcE5x+M1h7hubiUFkcFPXZGwV4XVlRj57QxF5MwpwRGREWHz/mMcOtbODfOrhrR9JKQSHJF8pgRHREaEFTUNmMGyeUNLcArCyQRHJTgi+UgJjogE5uiJThqbhzaX7mM1DVwytYzK0sIhbd9TRaUSHJG8pARHRALz3p9t5OqvPM4nH9jEtlj/80rFjrfzwt4mbhxi9RScTHA61QZHJC8pwRGRQMSb29mw8zCzqkp48Pl93PTtVdz5n8/w/J6m07ZdubUB5xhy+xuAaLINTpdKcETyUVBTNYhInlu5NYZz8PU3XcKksiLuXbeTnz3hTbewuLqcO6+fxfVzKzEzHq9pYMKYQi6cPGbIzx/1e1p1JZTgiOQjJTgiEogVNQ1UlnpJSyhkfPwV87hz2Szuf3o3P16zg3f/ZAPzJ5by3qXVrKmL89oFk08bvXggkZBfRaVGxiJ5SVVUIpJxnd0JVtfGuGFeFaHQyaSlpDDCe5ZWs/oTy/nmmxfQnXB87H9eoLm964za3wBEw+omLpLPVIIjIhm3Yedhjrd3sbyfpKUgEuK2y6fyxkunsGJrA8/sOsJ1cyvPaB/hkBEy6FIJjkheUoIjIhm3oqaBaNi4dk7FgNuFQsaN50/gxvP7n3tqIJFwiE61wRHJS6qiEpGMe7ymgcXV4ykpTO81VjRkKsERyVNKcEQko3Y1trAt1sLyIY5IfC4i4ZDa4IjkKSU4IpJRj9c0AGc2ps3ZioZD6kUlkqeU4IhIRj1e00B1RTEzKorTvq9o2DRVg0ieUoIjIhnT0t7FU9sPZ6T0BrzpGlRFJZKflOCISMasq4/T0Z3IWIITDYc0F5VInlKCIyIZ83hNAyWFERbNKM/I/qKhkKqoRPKUEhwRyQjnHCu2NnDd3AoKIpk59UTC6iYukq+U4IhIRmzef4xDx9oz0j08KRIO0aESHJG8pARHRDJihd89fFkGE5wCleCI5C0lOCKSEY9vbWDB1LFUlhZmbJ+RUIguTdUgkpeU4IhI2h1u6eD5PU0ZLb0Brw1Oh0pwRPKSEhwRSbtVtQ04l5nRi1MVhNWLSiRfBZLgmNldZrbPzJ73b6/utX66mTWb2ceDiE9EhteKmhgVJQVcPGVsRverXlQi+Su9U/kO7DvOuW/2s+7bwMOZDEZE0qOrO8Gq2hg3nT+BUMgyuu9IOESn2uCI5KUgE5w+mdnrgR1AS8ChiMgweH5PE0dPdGa8egq8KipN1SCSn4Jsg/MBM9tkZveY2TgAMysBPgn802APNrM7zGyjmW2MxWLpjlVEztLjNQ2EQ8a1cyoyvu9ISFVUIvkqbQmOmT1qZi/1cbsV+AEwC1gIHAC+5T/sLryqq+bBnt85d7dzbpFzblFlZWWaXoWInKvHaxpYdN44xo6KZnzfkXCITiU4InkpbVVUzrmbhrKdmf0IeMi/exVwm5l9HSgDEmbW5pz7fnqiFJF0OnD0BDUHj/PpV80PZP9RzSYukrcCaYNjZpOccwf8u28AXgJwzi1N2eYuoFnJjcjItaLGqz5eHkD7G/BmE1c3cZH8FFQj46+b2ULAATuBvwsoDhFJoxVbG5hSNoo5VSWB7D8SNjoTqqISyUeBJDjOuXcMYZu7MhCKiKRJe1c36+rjvPGyKZhltnt4UjSkXlQi+UojGYtIWjy94zCtHd2BdA9PioZDOAfdKsURyTtZNw6OiIw87/3ZRmLH2/mrK6fzmgWTGF0Q4fGaBgojIa6uznz38KRI2Cs56uxOEA6FA4tDRDJPCY6InJMDR0/w5y2HKC2K8IlfbeKLD23hDZdN4fGaBq6eNZ5RBcElFlE/welSCY5I3lGCIyLnZOVWr6fUA3cu4eiJTu57ahf3b9hDR1eCO66rDjS2SMirhe/sSkBhoKGISIYpwRGRc7KixuspNXdCCWbGlTPL+XxLB2vr49xy4YRAY4tG/ARH81GJ5B01MhaRs5bsKbVsXuUpPaXGFRfw2gWTKYwE2+4l6k/uqekaRPKPEhwROWsbdx6hpaOb5fOC6yk1kEjYL8FRV3GRvKMER0TO2oqaBgrCIZbMHh90KH2K9vSiUgmOSL5RgiMiZ23F1gauqi5ndEF2NueL+iU4XWqDI5J3lOCIyFnZc7iVbbEWlmVp9RRARG1wRPKWEhwROSsrtzYAsHxeZcCR9C/Zi6pDbXBE8o4SHBE5Kyu2xjhv/GhmVhQHHUq/ov44OCrBEck/SnBE5Iy1dXazfluc5fOqAptIcyiSUzV0qQRHJO8owRGRM/bk9kbaOhMsy+LqKTjZi0pVVCL5RwmOiJyxlVtjFEVDLK7Ozu7hST29qFRFJZJ3lOCISL+6uhM8tGk/B4+29SxzzvF4TQNLZlVQFM3uGbqTc1Gpm7hI/snOwStEJCv8/sUDfPj+5wkZXDO7gtsun8qcqlJ2H27lvUtnBh3eoDTQn0j+UoIjIv1aUdNAeXEBt181nV89u68n2QGyevybpKimahDJW0pwRKRPiYRjdV2c6+dW8tFb5vEPN83lyR2N/OqZfURCxrTy0UGHOKiTvahUgiOSb5TgiEifNu07yuGWjp6eUqGQsWRWBUtmVQQc2dD1lOCoDY5I3lEjYxHp04qaBsxg6Zzs7go+kORUDZ1dSnBE8o1KcESkTytrYyycVkZ5cUHQoZy15FQNXQlVUYkAfONPNfz2hf38xcWTecOlU5g3sTTokNJGCY6InKaxuZ1Ne5v4hxvnBh3KOUlO1aBeVCJeu7pfbtxLIuH40Zrt/PuqbVwwaQxvuHQKr1s4mQljioIOcVgpwRGR06ypi+McWT9S8WA0VYPISVsOHCN2vJ1vvnkBy+ZV8tAL+/nf5/bx5T+8zD8//DJLZo3n9Qun8MqLJlJaFA063HOmBEdETrNiawPjiwu4eMrYoEM5Jz1tcJTgiLCqNgbAdXMrqCgp5N3XzOTd18xkW6yZ3zy/nwef28f/eWAT//fBl7jp/AncunAyy+ZVURAZmc11leCIyCm6E47VtTGWzasiFMreiTSHwsyIho1OtcERYeXWBi6cPIaq0lOromZVlvDRm+fykZvm8NyeJh58bh8PbTrA7188wNhRUV598URet2AKV80sH1HnBCU4InKKTXubONLaOeKrp5IioZCqqCTvHT3RybO7m7jz+up+tzEzLps+jsumj+MfX3MBa+vj/Pb5/fzm+f384uk9TBxTxGsXTOLWhVO4cPIYzLI72QkkwTGzu4D3AjF/0Wecc3/w110C/BAYAySAK5xzbX09j4gMv5VbY4QMrhvB3cNTRcKmRsaS99bWxelOuCGPQB4Nh1g+r4rl86po7ejiz1sO8bsX9nPv+p38aM0OqiuKed3CybxuwWSqK0vSHP3ZCbIE5zvOuW+mLjCzCPBfwDuccy+Y2XigM5DoRPLUyq0NLJhWxrgR3D08VUE4pDY4kvdWbm1gTFGES6eVnfFjRxdEuHXhFG5dOIWm1g4efukgv3l+H997rI7vPlrHRVPG8NpLJvPaBZOZXDZq+IM/S9lWRXULsMk59wKAc64x4HhE8kpjczub9h0d8d3DU0XCpqkaJK8551hVG2PpnEoi4XNrMFw2uoC/vHI6f3nldA4ebeOhTfv53Qv7+crDNXzl4RqumDGO1y6YzKsumkRlaeEwvYKzE2SC8wEzeyewEfiYc+4IMBdwZvYnoBK43zn39b4ebGZ3AHcATJ8+PUMhi+S21XUxnIPl83Ojegq8NjgqwZF8tuXAMRqOt3P9MLermzi2iPcsreY9S6vZ1djC717Yz29f2M/nfrOZu367mSWzKnjtgkm84sKJlI3OfIlw2vp+mdmjZvZSH7dbgR8As4CFwAHgW/7DIsC1wNv9v28wsxv7en7n3N3OuUXOuUWVlblzMhYJ0sqtMSpKCrho8sjuHp6qIBJSLyrJayu3es1dl81N32/leeOL+cANc3jkI9fzp3+4jvcvn83eI6188lcvcsWXH2Vf04m07bs/aSvBcc7dNJTtzOxHwEP+3b3Aaudc3F/3B+Ay4LG0BCkiPZLdw5fnQPfwVJGQqReV5LVVtTEumDSGqgyNVDxvYinzJs7jozfPZfP+Y6ytjzMlgLY5gYzeY2aTUu6+AXjJ//9PwMVmNtpvcHw9sCXT8Ynko2T38OEuxg5aJBxSLyrJW8faOnlm15FAhn0wMy6aMpY7r5+V8X1DcG1wvm5mCwEH7AT+DsA5d8TMvg1s8Nf9wTn3+4BiFMkrK7fGRvzs4X0pCJva4EjeWneG3cNzSSAJjnPuHQOs+y+8ruIikkGramMsmDqyZw/vSyQcoiuhBEfy08qtMUqLIlw2vSzoUDJuZE4wISLD6nBLBy/sbeL6NDZCDEokpIH+JD+d7B5ecc7dw0ei/HvFInKaNX738FyZniFVVAP9SZ6qOXicg8faWDY3/6qnQAmOiACrtsYYNzrKJVPLgg5l2EU10J/kqZOzh+fehctQKMERyXOJhGN1nTfKaTiHuocnRVSCI3lq5dYG5k8sZeLYzHQPzzZKcETy3Ob9x4g3d+Rk+xvwS3A00J/kmeb2LjbuPJJzwz6cCSU4Ilmqpb2Lz/zvi/x0/U4OHE3fKKCrahuA3C3GVhscyUfr6+N0JVzetr+B7JtsU0R8j758iPue2g3A53+7mQVTx3LLhRN55UUTmVVZMmz7Wbk1xkVTxgQ+MV66REIhtcGRvLOqNkZxQZjLzxsXdCiBUYIjkqVWbo1RXlzAf9+xmEe2HOKRzQf5xp+28o0/bWV2VQm3XDCBV1w4kUumjsXs7NrOHG3t5NndR/j7ZbOHOfrsEdVAf5JnnHOs3BpjyewKCiL5W1GjBEckCyX8eaGum1PBnAmlzJlQyvuXz2Z/0wn+vOUQj2w5yA9Xb+f/rdzGxDFF3HLhBG6+YAJXzRx/Rie0tfVxEo6crqePKMGRNHPO8elfv0hBJMQtF0zkqupyogGOO7Mt1sK+phP8/fJgpkjIFkpwRLLQ5v3HaGzpOC3xmFw2inctmcG7lsygqbWDx2sa+NPmg/xy4x5+9sQuSosiLJ9XxS0XTuD6uZWUFkUH3M+q2gZKiyJcOq0sja8mWNGwqqgkvbYeOs79G/Zgxll9D4dbT/fwHJt25UwpwRHJQsmGvwPNC1U2uoA3XjaVN142lbbObtbWxXlky0EefbmB376wn2jYuHpWBTdfMIGbzq9i0thTZ/PNl1FOo+EQnZqqQdJo1VYvoVjxsWXUNTTzyOaDPFbjfQ8LwiEWzxrPzRdM4ObzJ2Sky/aq2hizKouZVj467fvKZkpwRLLQqtoYF08ZS0XJ0Br+FkXD3HTBBG66YALdCcczu47w6MuH+POWQ/zjgy/xjw/CxVPG+snOBM6fVErNweMcOtae870sIiEN9Cfptao2xvyJpcyoKGZGRTE3p3wP/7zlYMr38CUumTqWm8/3vqvzJ5aedfu5/rR1dvPU9kZuX3zesD7vSKQERyTLHD3RybO7m3jf9WdXfx4OGVfOLOfKmeV8+lXz2RZr5pEtXrLznUdr+fafa5lSNooJY7zkKVe7hydFwyG6Eg7n3LD/mIi0tHexYedh/uaamacsT/0efubV51Pf4H0PH335EN9+tJZv+d/D5EXHlTPLh6VB8BPbG2nvSuTsuFZnQgmOSD92NbZQVVrEqIJwRve7vj5Od8INS8NfM2N2VSmzq0r5+2WzaTjexoqaBv68pYG19TEunV6W86OcRsNeUtPZ7SiIKMGR4fXEtkY6u92ACYWZndJZIPV7eP+G3dy7fielhRGum1fJTedXsWxuFeOKC84qnlVbYxRFQ1w5s/xsX1LOUIIj0ocd8RZu/NZKouEQ18yu4Mbzq7hxfubqz9PV8LeqtIi3XjGdt14xnbbO7mF//myUbF/UlUhQoLFNZZitqo0xuiDM5TOGPt5M6vfwREc36+rjPFZziMdebuD3mw4QMlh0Xrl33jm/ilmVJUMufVxdG2Nx9XiKopm9MMtGSnBE+vB4TQMJB2+8bApr6+M8XtPAZ3mJCyeP4cb5Vdxw/gQumTKW0DDP3ZRs+Hvt7PQ3/M2XE2DE/4w6uxyc3UWxSL9W18VYMms8hZGz+z6NKjjZfi6RcLy47yiPvXyIR19u4CsP1/CVh2s4b/xobpjvXWQNVJW1u7GV7fEW3nm12t+AEhyRPq2ujVFdWcxX3ngJzjnqG5p59OUGHnv5EN9fUc+/PF5PRUkhy+dVcuP5VVw7p5KSwnP/OtU1NHPgaBv/cJPqz4dL8sdAPalkuO2Mt7CrsZX3XDtz8I2HIBQyFkwrY8G0Mj56yzz2N53g8RrvvPPzp3bzk3U7KSmMcO3sCm44v4rl86pOGYE82fvy+nm53XFgqJTgiPTS1tnNk9sb+aurpgOn1p+/b9ksjrR0sKo2xmP+GDT/88xeomGvQeEN8ydww/wqZlYUn9W+k91Nc73hbyZFQn4VlXpSyTBLjjdzfZp6Ik4uG8Xti8/j9sXn0drRxfr6Rh6raeDxmkP8cfNBABZMHcvy+VXcML+KlVtjTC8fzYzx+d09PEkJjkgvT+04PGAvhHHFBbz+0im8/tIpdHYneHbXER6vaeDxmga++NAWvvjQFmZWFLNsXiU3zK/iypnlQy6+XlUbY96E0tPGrJGzF+lpZKwSHBleq2pjzKwoZnoGEorRBZGeqiznLmLz/mOsqGng8a0NfO+xOr77aB0A77z6PPUW9CnBEelldW2MgkiIq2aOH3TbaDjEVdXjuap6PJ9+9fnsOdzKiq1esnOfX6Q8uiDMNbMrWDavkmXzqphS1nfy0tLexdM7DvPua2YM8yvKbwV+WyYlODKc2jq7eWJbI2+9YlrG921mXDRlLBdNGcsHb5xDY3M7q2pjbNh5WO1vUijBEellVW2Mq2aWn1X38Gnlo3nn1TN459UzONHRzRPbvQbKK2pi/HnLIQDmTihh+bwqrp9XyaLzTjYYfHJ7Ix3dGr9iuCVLcLoSqqKS4bNx5xFOdHZnxfd1fElhz6jmcpISHJEUe4+0Ut/QzNuG4apsVEHYb5Mzoaeh8sqtMVZsbeCedTv44ertlBRGWDJrPMvnV/HU9kZGRcMsOoPupjK4ZBscleDIcFpV2+CV9FZrvJlspQRHstKRlg5KiiIZn5F3dW0cgGXDPLt2akPl915XzfG2TtZva2Tl1hirtjbwiF+6c+P8qrPubip9Sw7u16lGxjKIzu4EX/lDDZWlhVw/t5LzJ/U/lUKypHd0gX5Gs5U+Gck6R1o6WPLVxwmHjKtnjef6uZVcP7cyIxPHra6NMXlsEbMqS9K6n9KiKK+4cCKvuHAizjnqGppZXx/nmtkVad1vPjrZi0olODKwDTsOc8+6HQB87Y81VJUWcp1//rl2dkXP6ML7m05Qe6iZtyzKfPsbGTolOJJ11tTHOdHZzasvnsgLe472tF2prijuOdlcVT38V06d3QnW1cd5zYJJGe2FYGbMnVDK3AmlGdtnPomEVYIjQ7OqNkY0bDzykevZuPMwq2q9tnMPPLMXM7hkahnXz62k3R8FPBva30j/Bv2FMLO5wA+ACc65i8zsEuB1zrkvpT06yUurtsYoGx3lX//yMkIG2+MtrNoaY1VtrGfeloJwiCtmjuO6OZVcN7dyWGblfW53E8fbu3TSyjHRlKkaRAayqjbGovPKmVlRzMyKYt68aBrdCcfze5pYXRtjdV2M7z9eR8LB5LFFzK5Kb0mvnJuhXAL/CPg/wA8BnHObzOw+QAmODDvnHGvqvKkKwv4Q+7MqS5hVWcLfXDuTts5uNuw8zOpaL+FJDmVeWVrI0jkVXDenkmvnVFBRUjjInk63ujZGOGQsUTVRTomqm7gMwaFjbdQcPM4nXzn/lOXhkHH5eeO4/LxxfOTmuTS1drCuvpHJZUUabybLDSXBGe2ce7rXB9l1Ljs1s7uA9wIxf9FnnHN/MLMo8GPgMj+2nznnvnIu+5KRpebgcRqOt/c7km9RNMzSOZUsnVPJZ/8CDh5tY01djNV1cVbUNPDrZ/cBcP6kMVw3p4KlcypZNGPckOZdWlUb4/Lp4xhTFB3W1yTB6pmLSlVUMoDVPaMSD1yCWza6gL+4ZFImQpJzNJQEJ25mswAHYGa3AQeGYd/fcc59s9eyNwOFzrmLzWw0sMXMfuGc2zkM+5MRYKgnmaSJY4t486JpvHnRNBIJx0v7j7KmLs6aulhPV+zCSIgrZ5Zz7ewKrp1TwfkTx5w2SWa8uZ0X9x3l47fMHfbXJMHqqaJSgiMDWF0Xp7K0kPMnqS1crhhKgvN+4G5gvpntA3YAt6cpHgcUm1kEGAV0AMfStC/JQqtqY8yfWMqEMUVn/NhQyLhkahmXTC3j/ctn94wMvLouxtq6OF95uAYehoqSApbM8pKda2dXMLlsFGvrvO7h6ZpTRoIT1VQNMojuhFc1fsP8KlU75ZBBExzn3HbgJjMrBkLOuePDtO8PmNk7gY3Ax5xzR4AHgFvxSohGAx9xzh3u68FmdgdwB8D06dOHKSQJUmtHFxt3Hhm2qQqKCyMsn1/F8vle0nLwaBtr6+OsrYuxtr6R376wH4DqSm9izPHFBVw4ecyw7Fuyh9rgyGBe3HeUptZOdTDIMUPpRfXRXvcBjgLPOOeeH+BxjwIT+1j1WbxeWV/EK7H5IvAt4G+AK4FuYDIwDlhjZo/6SdYpnHN345UssWjRIpU954DkVAXXzUnPSWbi2CJuu3wqt10+FeccWw8dZ21dnHX1cZ7acZjbLp96WtWVjHyaqkEGs7o2hhksTdO5R4IxlCqqRf7td/791wCbgDvN7H+cc1/v60HOuZuGEoCZ/Qh4yL/7V8AfnXOdQIOZrfP3fVqCI7ln1dZYxqYqMDPmTxzD/IljeM/SahIJp+QmR2mqBhnMqtoYF08ZS7k/kJ/khqGMgz8VuMw59zHn3MeAy4Eq4Drg3WezUzNLbYL+BuAl///dwA3+NsXAYqDmbPYhI8/qujiLq8uH1ONpuCm5yV0nZxNXCY6c7uiJTp7f06TqqRw0lASnCmhPud+JN+jfiV7Lz8TXzexFM9sELAc+4i//N6DEzDYDG4CfOOc2neU+ZATZ3djKjnhLv93DRc5WTxWVSnCkD+vr43QnnM49OWgoVVQ/B54ys9/4918L3OeXsGw5m506597Rz/JmvK7ikmdW1Z1Z93CRoVIbHBnIqtoYpYURFk4rCzoUGWZD6UX1RTP7I7DEX3Snc26j///b0xaZ5JXVtTGmjhvFzIrioEORHBP12+B0dKkER07lnGN1bYxrZlf09LaT3DGk2QqdcxvMbBdQBGBm051zu9MameSNjq4ET2xr5HULJ2sMChl2oZARDpnmopLTbIs1s/9oGx+4QSXHuWgo3cRfh9eNezLQAEzHa/h7YXpDk3zx7O4jNGuSS0mjSMg0knGe+5+Ne/jmI1tZOK2MxdXjWVw9vmeAz+vmav65XDSUEpwv4vVmetQ5d6mZLSd9IxlLHlpdGyMSMpbMGh90KJKjCsIhOtTIOK/96tm9dHQl2Lz/GH/afKhn+azKYqaOGx1gZJIuQ0lwOp1zjWYWMrOQc26FmX033YFJ/lhdF+Oy6eMo1SSXkiaRsEpw8llLexfP7DrC31w7k0+/6nz2Hmnlqe2H2bDzsHpP5bChJDhNZlYCrAZ+bmYNQEt6w5J8EW9u56V9xzTJpaRVJBxSG5w89uT2Rjq7Xc8o6VPHjWbq5aN50+VTA45M0mkozcZvBVrxxqr5I7ANbzRjkXO2rj5ZB66rKEmfaMg00F8eW12buVHSJXsMJcH5nHMu4Zzrcs791Dn3L8An0x2Y5IdVtTHGjY5y4eSxQYciOSwaCWmqhjyWHCW9MJL5UdIlOENJcG7uY9mrhjsQyT/OOdbUxbl2TiVhTZUgaaReVPlrz2FvlHRNpJl/+m2DY2bvA/4eqPanVEgqBdalOzDJfTUHjxM73s7SOeqiKekVDasEJ1+t9kdJVzV4/hmokfF9wMPAV4BPpSw/7pw7nNaoJC+srvVPPLqykjRTgpO/VtfGmFI2ilmVGiU93wxURRUGjgHvB46n3DCz8vSHJrluTV2ceRNKmTi2KOhQJMdFwqa5qPJQZ3eC9fWNLJ1ToVHS89BAJTjPAMkzQu8jwwHVaYlI8sKJjm6e3nmYdy4+L+hQJA9EQyrByUcv7GnieHuXqqfyVL8JjnNuZiYDkfzy5I5GOroSOvFIRkTCpsk289Dq2hghg2tmqZ1fPhrSZJv+fFTX+XdXOuceSl9Ikg/W1MYpjIS4cqZqOyX9ouEQLe1dQYchGbaqLs6CaWWMHa1R0vPRoN3EzeyrwIeBLf7tw2b2z+kOTHLb6roYV84spyiqcSkk/aJhDfSXb460dLBpb5M6MeSxoZTgvBpY6JxLAJjZT4HngM+kMzDJXfubTlDf0MzbrpgWdCiSJyIhTdWQb9Zti+Ocuofns6EM9AdQlvK/hpwVTnR0U3foOF1n0XBzjT8uhQbekkzxRjJWCU4+WV0bo7QowoKp+snKVwMN9PdvwC+AfwaeNbOVeL2pruPUcXEkD33l4Zf52RO7GBUNc/HUsVw6rYyF08pYOL2MSWNHDfjY1bVxJo4pYu6EkgxFK/nOm4tKJTj5wjnH6to4186uIBIe6nW85JqBqqhqgW8Ak4DHgJ3A88AnnXMH0x6ZZC3nHI+93MCCaWVcOq2M5/Y08ZN1O+nwf0AmjClkwVQv2Vk4tYyLp46ltMhr5NedcKytj3PLBRM0LoVkTCSsqRrySX1DMwePtal6Ks8N1E38e8D3zOw84G3+7e3AfWb2C+dcXYZilCyzI97CvqYT/P3yWbz9Km8cm/aubrbsP8YLe5p4fk8TL+w9yiNbDgFgBrMqS1gwtYwJYwo5eqKTpTrxSAZFwmqDk09W18UBuHa2uofns0EbGTvndgFfA75mZpcC9wCfxxvpWPJQX1MsFEbCXDp9HJdOH9ezrKm1gxf2HuWFPU28sKeJVbUNxJs7KAiHdOKRjCoIhzQOTh5ZUxejuqKYaeWjgw5FAjRogmNmEbzZw98G3AisBO5Ka1SS1dbUxZkxfvSgJ4+y0QVcP7eS6/3SGucc+5pO0NaZoLy4IBOhigD+bOKaqiEvtHd18+T2Rt66SL00891AjYxvBv4Sr5v408D9wB3OuZYMxSZZqKMrwRPbG3nTZVPP+LFmxtRxuqKSzIuEQ2qDkyee2XmEtk6Nki4Dl+B8Gm9G8Y85545kKB7Jcs/uPkJrRzdL56iKSUaOgrDR0Z3AOafG7TludV2caNhYXD0+6FAkYAM1Mr4hk4HIyLCmLkY4ZFw9SycPGTmSXYW7E45IWAlOLltTF+Oy6eMoLhzSTESSwzRAgJyRNXVxLpte1tPtW2QkSCY1aoeT2+LN7Wzef0zVUwIEmOCY2QfNrMbMNpvZ11OWf9rM6s1sq5m9Iqj45HSHWzp4cd9RjUAsI06BX4LTocH+ctq6eq97uKrQBYY4m/hwM7PlwK3AAudcu5lV+csvwOutdSEwGXjUzOY657qDiFNOta7em9tFJw8ZaSIhvwRHDY1z2uraOONGR7lwsqZnkOBKcN4HfNU51w7gnGvwl98K3O+ca3fO7QDqgSsDilF6WVMXY0xRhEumlgUdisgZSbbBOZu502RkcM6xpi7GNbMrCIfUzkqCS3DmAkvN7CkzW2VmV/jLpwB7Urbb6y87jZndYWYbzWxjLBZLc7jinTziXDtHJw8ZeaJ+G5xOtcHJWbWHmmk43n7KAKSS39JWRWVmjwIT+1j1WX+/5cBi4Argl2ZWfSbP75y7G7gbYNGiRTprpdm2WDMHjrbxIZ08ZASK+iU4nRrNOGetqfMudK9VFbr40pbgOOdu6m+dmb0P+LVzzgFPm1kCqAD2AanDT071l0nAVtdqbhcZuXqqqDQfVc5aXRdndlUJk8tGBR2KZImgqqgeBJYDmNlcoACIA78F3mZmhWY2E5iDN4qyBExzu8hIFvWrVTvVyDirbYs1s7uxlcQZViW2dXbz1PZGdYCQUwQ1EtI9wD1m9hLQAbzLL83ZbGa/BLYAXcD71YMqsxIJx7t+8jQt7V3MnzSG8yeNYW5VCU9uP8xbFp359Awi2aCnikqNjLPW4ZYOXvXdNXR0JyguCDNvYinnTxrjnYcmljJvYmm/429t3HmE9q6E2t/IKQJJcJxzHcDt/az7MvDlzEYkSVsOHGNNXZxZlcU89MJ+7ntqd886jX8jI1VyoD+V4GSvtfVxOroTfOiG2Rxr62LLgWP87oX9/DzlHDStfBTzJ45h/sRS5k8cw7yJpcwYP5o1dTGiYeOq6vIAX4FkG41lLadY6w+Udd97F1NVWsiBo23UHDxG/HgHy+dXBRydyNmJqpt41lvrD0Px4Zvm9vTUdM6x/2gbNQeOUXPwOC8fOMbLB47x2MuHSNZiFUZCmMGi88oZXaCfNDlJR4OcYk1djHkTSpkwpgiAyWWj1GhPRryegf7UTTwrOedYWxc/bQwbM2NK2SimlI3ixvMn9Cxv6+ymvqGZmoPH2XrwGHUNzdx+1XlBhC5ZTAmO9Gjr7GbDziO8Y7FOFJJbohFN1ZDNtsVa2H+0jfffMLRGwkXRMBdNGctFUzRisfRPk21Kj6d3HKajK6GeCJJzoqFkFZVKcLLRWn8Mm6Wz1c5Pho8SHOmxpi5GQTjEVTPHBx2KyLDqmU1cJThZaU1dnPPGj2b6eA1DIcNHCY70WFMXZ9GMcYwqCAcdisiwimo28azV2Z3gye2NGkRUhp0SHAGg4XgbNQePa5hzyUnRsGYTz1bP7W6ipaNbw1DIsFOCIwCs87uHa6AsyUWaqiF7ramLETK4epaqxmV4KcERwKueKi8u4IJJY4IORWTYaaqG7LWmLs6CaWWMHdX3KMUiZ0sJjvSMQbFk1nhCKWNQiOQKTdWQnY62drJpb5OqpyQtlOAItYeaaTjeruopyVkRtcHJSuu3xUk4NDSFpIUSHGGNPwaFGhhLruopwVEbnKyypj5OSWGEhdPKgg5FcpASHOmZXFNTMkiu6klwulSCk03W1MVYXD2+5/MRGU46qvJce1c3T+1oVB245LRwyDBTL6pssquxhT2HT6h6StJGc1HlqOb2Lt76wycImTGzopgZFcVU+39nVhT39Fh4ZucR2jo1PYPkvmgopF5UWWRNnTc0hc49ki5KcHLUE9sa2bz/GJdOL+O5PUf43ab9uJRze3lxATMrijnR0U0kZFxVrTEoJLdFw6apGrLImroYU8pGMbOiOOhQJEcpwclRa+tijIqGuf+OxRRGwrR1drPncCvb4y3sjLews7GFHfEW9rV08JpLJlFSqENBclskHFI38SzR1Z1g/bZGXn3RJMw0NIWkh37VctSa+jhXziynMOLNK1UUDTNnQilzJpQGHJlIMKJhozOhKqps8OK+oxxv61LPTUkrNTLOQfubTrA91qK6bZEUkVBIVVRZYq3f/uYaTbApaaQEJwet9eeV0tWRyEnRiKmRcZZYUx/nwsljKC8uCDoUyWFKcHLQ2ro4FSWFzFN1lEgPrxeVSnCC1tLexXO7j+gCTNJOCU6OSSQc6+rjXDt7vBrviaSIhE1TNWSBp3ccprPbsXS2xt6S9FKCk2NePniMxpYOrtXAfSKniIZDGugvC6ypi1MYCbFoxrigQ5EcpwQnx6xLtr9R4z2RU0TCITpUghO4tfUxrphRTlE0HHQokuOU4OSYNXVx5lSVMHFsUdChiGSVaEgD/QWt4VgbtYea1f5GMkIJTg5p6+zm6R2H1fVSpA9qgxO8tSphlgxSgpNDntl1hPYuzSsl0pdoOESHSnACtbYuTnlxARdMGhN0KJIHAktwzOyDZlZjZpvN7Ov+spvN7Bkze9H/e0NQ8Y1Ea+vjmldKpB9qZBws5xxr6+MsmTWeUEg9PCX9ApmqwcyWA7cCC5xz7WZW5a+KA691zu03s4uAPwFTgohxJFpbF+ey6eM0r5RIHyIhVVEFqa6hmYbj7SphlowJqgTnfcBXnXPtAM65Bv/vc865/f42m4FRZlYYUIwjypGWDl7af1Ttb0T6EY2oiipIa+qSI6xrCAvJjKASnLnAUjN7ysxWmdkVfWzzJuDZZBL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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# initialize network\n",
    "network = Network()\n",
    "network = Network()\n",
    "\n",
    "# set number of neurons\n",
    "num_inputs = 1\n",
    "num_neurons = 1\n",
    "\n",
    "# simulation time\n",
    "time = 100\n",
    "dt = 1\n",
    "\n",
    "# configure weights for the synapses between the input layer and LIF layer\n",
    "w = torch.round(torch.abs(2 * torch.randn(num_inputs, num_neurons)))\n",
    "\n",
    "# create input spike data, where each spike is distributed according to Bernoulli(0.1)\n",
    "input_data = torch.bernoulli(0.3 * torch.ones(time, num_inputs)).byte()\n",
    "inputs = {\"Input Layer\": input_data}\n",
    "\n",
    "# initialize list of neuron models\n",
    "neuron_models = [IFNodes(n=num_neurons),LIFNodes(n=num_neurons),AdaptiveLIFNodes(n=num_neurons),SRM0Nodes(n=num_neurons)]\n",
    "neuron_types = [\"IF Neuron\",\"LIF Neuron\",\"Adaptive LIF Neuron\",\"SRM0 Neuron\"]\n",
    "spikes={}\n",
    "voltages={}\n",
    "\n",
    "s_ims = None\n",
    "s_axes = None\n",
    "v_ims = None\n",
    "v_axes = []\n",
    "\n",
    "for neuron_model_index in range(len(neuron_models)):\n",
    "    # initialize input and neuron layers\n",
    "    input_layer = Input(n=num_inputs)\n",
    "    neuron_layer = neuron_models[neuron_model_index]\n",
    "\n",
    "    # connection between the input layer and the LIF layer\n",
    "    connection = Connection(\n",
    "        source=input_layer, target=neuron_layer,w=w\n",
    "    )\n",
    "\n",
    "    # create a monitor\n",
    "    neuron_layer_monitor = Monitor(\n",
    "        obj=neuron_layer,\n",
    "        state_vars=(\"s\", \"v\"),  # Record spikes and voltages.\n",
    "        time=time,  # Length of simulation (if known ahead of time).\n",
    "    )\n",
    "\n",
    "    # add layers to network\n",
    "    network.add_layer(\n",
    "        layer=input_layer, name=\"Input Layer\"\n",
    "    )\n",
    "    network.add_layer(\n",
    "        layer=neuron_layer, name=\"Neuron Layer\"\n",
    "    )\n",
    "\n",
    "\n",
    "    # add connection to network\n",
    "    network.add_connection(\n",
    "        connection=connection, source=\"Input Layer\", target=\"Neuron Layer\"\n",
    "    )\n",
    "\n",
    "    # add monitor to the network\n",
    "    network.add_monitor(monitor=neuron_layer_monitor, name=\"Neuron Layer\")\n",
    "\n",
    "    # simulate network on input data\n",
    "    network.run(inputs=inputs, time=time)\n",
    "\n",
    "    # retrieve and plot simulation spike, voltage data from monitors\n",
    "    # s_ims, s_axes = plot_spikes({neuron_types[neuron_model_index] : neuron_layer_monitor.get(\"s\")},ims=s_ims,axes=s_axes)\n",
    "    v_axes = plot_voltages({neuron_types[neuron_model_index] : neuron_layer_monitor.get(\"v\")}, plot_type=\"line\",axes=v_axes)\n",
    "\n",
    "\n",
    "# plot spikes and voltages of the LIF layer\n",
    "# TODO: plot axes\n",
    "# plot_spikes(spikes)\n",
    "# plot_voltages(voltages, plot_type=\"line\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "## 5. Spike Traces\n",
    "\n",
    "### Overview\n",
    "\n",
    "![Neuron Spike Trace](http://www.scholarpedia.org/w/images/3/3e/STDP-Fig2.png)\n",
    "\n",
    "Top: A presynaptic spike leaves a trace $x_j(t)$ which is read out (arrow) at the moment of the postsynaptic spike. The weight change is proportional to that value $x_j(t_n)$ \n",
    "\n",
    "Bottom: A postsynaptic spike leaves a trace $y(t)$ which is read out (arrow) at the moment of a presynaptic spike.\n",
    "\n",
    "Source: http://www.scholarpedia.org/article/Spike-timing_dependent_plasticity\n",
    "\n",
    "### BindsNET Traces\n",
    "\n",
    "In BindsNET, each node layer has the following variables to keep track of the node traces:\n",
    "\n",
    "| Neuron Layer Variable | Description | Shape | \n",
    "| - | - | - | \n",
    "| `Nodes.s` | spike occurances at the current time step | `batch_size` x `n` |\n",
    "| `Nodes.x` | firing traces; set to 1 wherever `Node.s` is 1, otherwise multiplied by `Nodes.trace_decay` | `batch_size` x `n` |\n",
    "| `Nodes.tc_trace` | time constant for trace decay | 1 |\n",
    "| `Nodes.trace_decay` | spike trace decay per time step: $e^{{-dt}/\\tau}$ | 1 |\n",
    "| `Nodes.traces_additive` | spike traces are added to the previous value and not maxed out at 1 | 1 |\n",
    "\n",
    "At each time step `dt`:\n",
    "\n",
    "1. Node membrane potentials `Nodes.v` are updated based on the input `x`\n",
    "\n",
    "2. Node spikes are recorded in `Nodes.s`\n",
    " \n",
    "3. Node traces `Nodes.x` are multiplied by the `Nodes.trace_decay`\n",
    "\n",
    "4. Node traces `Nodes.x` are set to 1 wherever spikes have occured (or 1's are added if `Nodes.traces_additive` is set to `True`)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  }
 ]
}