Fix raster plot physical units in label

doc/htmldoc/faqs/qa-precise-spike-times.rst

@@ -140,7 +140,7 @@ Questions and answers about precise neurons
 13. Q: **What is the rate at which spikes are missed in a typical
     large-scale neuronal network simulation of integrate-and-fire model
     neurons with linear subthreshold dynamics in the balanced state and
-    a spike rate of around 10 Hz**?
+    a spike rate of around 10 spks/s**?
 
     A: At a typical parameter setting for a simulation with around 10,000
     neurons and 15 million synapses, the total rate at which spikes are

doc/htmldoc/ref_material/glossary.rst

@@ -41,22 +41,19 @@ Units of measure
    Nanosiemens (nS).
 
  g_L
-   Leak conductance in Nanosiemens (nS).
+   Leak conductance in nanosiemens (nS).
 
  g_K
-   Potassium peak conductance in Nanosiemens (nS).
+   Potassium peak conductance in nanosiemens (nS).
 
  g_Na
-   Sodium peak conductance in Nanosiemens (nS).
+   Sodium peak conductance in nanosiemens (nS).
 
  spike rates
-   Spikes/second.
-
- modulation frequencies
-   Herz (Hz).
+   The number of spikes that occurred in a certain time interval, usually expressed in terms of spikes per second (spks/s or s^-1).
 
  frequency
-   Frequncy in Hertz (Hz).
+   Frequency in Hertz (Hz). Note that spike rates are often better expressed in terms of spikes per second (spks/s or s^-1).
 
  voltage
    Millivolts (mV).

models/mip_generator.cpp

@@ -44,7 +44,7 @@ nest::register_mip_generator( const std::string& name )
  * ---------------------------------------------------------------- */
 
 nest::mip_generator::Parameters_::Parameters_()
-  : rate_( 0.0 ) // Hz
+  : rate_( 0.0 ) // spks/s
   , p_copy_( 1.0 )
 {
 }
@@ -114,7 +114,7 @@ nest::mip_generator::pre_run_hook()
 {
   StimulationDevice::pre_run_hook();
 
-  // rate_ is in Hz, dt in ms, so we have to convert from s to ms
+  // rate_ is in spks/s, dt in ms, so we have to convert from s to ms
   poisson_distribution::param_type param( Time::get_resolution().get_ms() * P_.rate_ * 1e-3 );
   V_.poisson_dist_.param( param );
 }

models/mip_generator.h

@@ -151,7 +151,7 @@ class mip_generator : public StimulationDevice
    */
   struct Parameters_
   {
-    double rate_;   //!< process rate in Hz
+    double rate_;   //!< process rate in spks/s
     double p_copy_; //!< copy probability for each spike in the parent process
 
     Parameters_(); //!< Sets default parameter values

models/poisson_generator.cpp

@@ -48,7 +48,7 @@ nest::register_poisson_generator( const std::string& name )
  * ---------------------------------------------------------------- */
 
 nest::poisson_generator::Parameters_::Parameters_()
-  : rate_( 0.0 ) // Hz
+  : rate_( 0.0 ) // spks/s
 {
 }
 
@@ -112,7 +112,7 @@ nest::poisson_generator::pre_run_hook()
 {
   StimulationDevice::pre_run_hook();
 
-  // rate_ is in Hz, dt in ms, so we have to convert from s to ms
+  // rate_ is in spks/s, dt in ms, so we have to convert from s to ms
   poisson_distribution::param_type param( Time::get_resolution().get_ms() * P_.rate_ * 1e-3 );
   V_.poisson_dist_.param( param );
 }

models/poisson_generator_ps.cpp

@@ -51,7 +51,7 @@ nest::register_poisson_generator_ps( const std::string& name )
  * ---------------------------------------------------------------- */
 
 nest::poisson_generator_ps::Parameters_::Parameters_()
-  : rate_( 0.0 )      // Hz
+  : rate_( 0.0 )      // spks/s
   , dead_time_( 0.0 ) // ms
   , num_targets_( 0 )
 {

models/poisson_generator_ps.h

@@ -148,7 +148,7 @@ class poisson_generator_ps : public StimulationDevice
    */
   struct Parameters_
   {
-    double rate_;      //!< process rate [Hz]
+    double rate_;      //!< process rate [spks/s]
     double dead_time_; //!< dead time [ms]
 
     /**

models/sinusoidal_poisson_generator.cpp

@@ -281,7 +281,7 @@ nest::sinusoidal_poisson_generator::update( Time const& origin, const long from,
         kernel().event_delivery_manager.send( *this, se, lag );
       }
     }
-    // store rate in Hz
+    // store rate in spks/s
     B_.logger_.record_data( origin.get_steps() + lag );
   }
 }

models/step_rate_generator.h

@@ -53,7 +53,7 @@ The ``rate_generator`` provides a piecewise constant rate input to the
 connected rate unit(s). Please note that this input is handled in the same
 way as input from any other rate unit, that is, it is processed by the input
 function of the receiving rate unit. The amplitude of the rate is changed
-at the specified times. The unit of the rate is Hz.
+at the specified times. The unit of the rate is spks/s.
 
 If ``allow_offgrid_times`` is false, times will be rounded to the nearest
 grid point if they are less than tic/2 from the grid point, otherwise

nestkernel/growth_curve.h

@@ -104,7 +104,7 @@ class GrowthCurve
  *                          rate that the neuron should achieve.  For example, an
  *                          eps = 0.05 [Ca2+] with tau_Ca = 10000.0 and beta_Ca =
  *                          0.001 for a synaptic element means a desired firing
- *                          rate of 5Hz.
+ *                          rate of 5 spks/s.
  *
  *  References:
  *   [1] Butz, Markus, Florentin Wörgötter, and Arjen van Ooyen.
@@ -184,7 +184,7 @@ class GrowthCurveLinear : public GrowthCurve
  *                          firing rate that the neuron should achieve.  For
  *                          example, an eps = 0.05 [Ca2+] with tau_Ca = 10000.0
  *                          and beta_Ca = 0.001 for a synaptic element means a
- *                          desired firing rate of 5Hz.
+ *                          desired firing rate of 5 spks/s.
  *
  *   nu           double -  Growth rate in elements/ms. The growth rate nu is
  *                          defined in the SynapticElement class. Can be negative.
@@ -261,7 +261,7 @@ class GrowthCurveGaussian : public GrowthCurve
  *                          firing rate that the neuron should achieve.  For
  *                          example, an eps = 0.05 [Ca2+] with tau_Ca = 10000.0
  *                          and beta_Ca = 0.001 for a synaptic element means a
- *                          desired firing rate of 5Hz.
+ *                          desired firing rate of 5 spks/s.
  *
  *   nu           double -  Growth rate in elements/ms. The growth rate nu is
  *                          defined in the SynapticElement class. Can be negative.

nestkernel/structural_plasticity_node.h

@@ -130,7 +130,7 @@ class StructuralPlasticityNode : public Node
    *
    * Intracellular calcium concentration has a linear factor to mean
    * electrical activity of 10^2, this means, for example, that a [Ca2+] of
-   * 0.2 is equivalent to a mean activity of 20 Hz.
+   * 0.2 is equivalent to a mean activity of 20 spks/s.
    */
   double Ca_minus_;
 

pynest/examples/astrocytes/astrocyte_interaction.py

@@ -134,7 +134,7 @@
 axes[1].plot(data_astro["times"], data_astro["IP3"])
 axes[2].plot(data_post["times"], data_post["I_SIC"])
 axes[3].plot(data_astro["times"], data_astro["Ca_astro"])
-axes[0].set_title(f"Presynaptic neuron\n(Poisson rate = {poisson_rate_neuro} Hz)")
+axes[0].set_title(f"Presynaptic neuron\n(Poisson rate = {poisson_rate_neuro} spks/s)")
 axes[0].set_ylabel("Membrane potential (mV)")
 axes[2].set_title("Postsynaptic neuron")
 axes[2].set_ylabel("Slow inward current (pA)")

pynest/examples/balancedneuron.py

@@ -123,13 +123,13 @@
 
 
 def output_rate(guess):
-    print("Inhibitory rate estimate: %5.2f Hz" % guess)
+    print("Inhibitory rate estimate: %5.2f spks/s" % guess)
     rate = float(abs(n_in * guess))
     noise[1].rate = rate
     spikerecorder.n_events = 0
     nest.Simulate(t_sim)
     out = spikerecorder.n_events * 1000.0 / t_sim
-    print("  -> Neuron rate: %6.2f Hz (goal: %4.2f Hz)" % (out, r_ex))
+    print("  -> Neuron rate: %6.2f spks/s (goal: %4.2f spks/s)" % (out, r_ex))
     return out
 
 
@@ -143,13 +143,13 @@ def output_rate(guess):
 # During simulation, the ``spike_recorder`` counts the spikes of the target
 # neuron and the total number is read out at the end of the simulation
 # period. The return value of ``output_rate()`` is the firing rate of the
-# target neuron in Hz.
+# target neuron in spks/s.
 #
 # Second, the scipy function ``bisect`` is used to determine the optimal
 # firing rate of the neurons of the inhibitory population.
 
 in_rate = bisect(lambda x: output_rate(x) - r_ex, lower, upper, xtol=prec)
-print("Optimal rate for the inhibitory population: %.2f Hz" % in_rate)
+print("Optimal rate for the inhibitory population: %.2f spks/s" % in_rate)
 
 ###############################################################################
 # The function ``bisect`` takes four arguments: first a function whose

pynest/examples/brunel_alpha_nest.py

@@ -154,7 +154,7 @@ def ComputePSPnorm(tauMem, CMem, tauSyn):
 # Definition of threshold rate, which is the external rate needed to fix the
 # membrane potential around its threshold, the external firing rate and the
 # rate of the poisson generator which is multiplied by the in-degree CE and
-# converted to Hz by multiplication by 1000.
+# converted to spks/s by multiplication by 1000.
 
 nu_th = (theta * CMem) / (J_ex * CE * np.exp(1) * tauMem * tauSyn)
 nu_ex = eta * nu_th
@@ -281,7 +281,7 @@ def ComputePSPnorm(tauMem, CMem, tauSyn):
 # Calculation of the average firing rate of the excitatory and the inhibitory
 # neurons by dividing the total number of recorded spikes by the number of
 # neurons recorded from and the simulation time. The multiplication by 1000.0
-# converts the unit 1/ms to 1/s=Hz.
+# converts the unit 1/ms to 1/s.
 
 rate_ex = events_ex / simtime * 1000.0 / N_rec
 rate_in = events_in / simtime * 1000.0 / N_rec
@@ -310,8 +310,8 @@ def ComputePSPnorm(tauMem, CMem, tauSyn):
 print(f"Number of synapses: {num_synapses}")
 print(f"       Excitatory : {num_synapses_ex}")
 print(f"       Inhibitory : {num_synapses_in}")
-print(f"Excitatory rate   : {rate_ex:.2f} Hz")
-print(f"Inhibitory rate   : {rate_in:.2f} Hz")
+print(f"Excitatory rate   : {rate_ex:.2f} spks/s")
+print(f"Inhibitory rate   : {rate_in:.2f} spks/s")
 print(f"Building time     : {build_time:.2f} s")
 print(f"Simulation time   : {sim_time:.2f} s")
 

pynest/examples/brunel_delta_nest.py

@@ -233,7 +233,7 @@
 # Calculation of the average firing rate of the excitatory and the inhibitory
 # neurons by dividing the total number of recorded spikes by the number of
 # neurons recorded from and the simulation time. The multiplication by 1000.0
-# converts the unit 1/ms to 1/s=Hz.
+# converts the unit 1/ms to 1/s.
 
 rate_ex = events_ex / simtime * 1000.0 / N_rec
 rate_in = events_in / simtime * 1000.0 / N_rec
@@ -262,8 +262,8 @@
 print(f"Number of synapses: {num_synapses}")
 print(f"       Excitatory : {num_synapses_ex}")
 print(f"       Inhibitory : {num_synapses_in}")
-print(f"Excitatory rate   : {rate_ex:.2f} Hz")
-print(f"Inhibitory rate   : {rate_in:.2f} Hz")
+print(f"Excitatory rate   : {rate_ex:.2f} spks/s")
+print(f"Inhibitory rate   : {rate_in:.2f} spks/s")
 print(f"Building time     : {build_time:.2f} s")
 print(f"Simulation time   : {sim_time:.2f} s")
 

pynest/examples/brunel_exp_multisynapse_nest.py

@@ -130,7 +130,7 @@
 # Definition of threshold rate, which is the external rate needed to fix the
 # membrane potential around its threshold, the external firing rate and the
 # rate of the poisson generator which is multiplied by the in-degree CE and
-# converted to Hz by multiplication by 1000.
+# converted to spks/s by multiplication by 1000.
 
 nu_th = theta / (J * CE * tauMem)
 nu_ex = eta * nu_th
@@ -265,7 +265,7 @@
 # Calculation of the average firing rate of the excitatory and the inhibitory
 # neurons by dividing the total number of recorded spikes by the number of
 # neurons recorded from and the simulation time. The multiplication by 1000.0
-# converts the unit 1/ms to 1/s=Hz.
+# converts the unit 1/ms to 1/s.
 
 rate_ex = events_ex / simtime * 1000.0 / N_rec
 rate_in = events_in / simtime * 1000.0 / N_rec
@@ -294,8 +294,8 @@
 print(f"Number of synapses: {num_synapses}")
 print(f"       Excitatory : {num_synapses_ex}")
 print(f"       Inhibitory : {num_synapses_in}")
-print(f"Excitatory rate   : {rate_ex:.2f} Hz")
-print(f"Inhibitory rate   : {rate_in:.2f} Hz")
+print(f"Excitatory rate   : {rate_ex:.2f} spks/s")
+print(f"Inhibitory rate   : {rate_in:.2f} spks/s")
 print(f"Building time     : {build_time:.2f} s")
 print(f"Simulation time   : {sim_time:.2f} s")
 

pynest/examples/brunel_siegert_nest.py

@@ -202,5 +202,5 @@
 rates_ex = data["rate"][numpy.where(data["senders"] == siegert_ex.global_id)]
 rates_in = data["rate"][numpy.where(data["senders"] == siegert_in.global_id)]
 times = data["times"][numpy.where(data["senders"] == siegert_in.global_id)]
-print(f"Excitatory rate   : {rates_ex[-1]:.2f} Hz")
-print(f"Inhibitory rate   : {rates_in[-1]:.2f} Hz")
+print(f"Excitatory rate   : {rates_ex[-1]:.2f} spks/s")
+print(f"Inhibitory rate   : {rates_in[-1]:.2f} spks/s")

pynest/examples/gap_junctions_inhibitory_network.py

@@ -143,11 +143,11 @@
 spikes = events["senders"]
 n_spikes = sr.n_events
 
-hz_rate = (1000.0 * n_spikes / simtime) / n_neuron
+spike_rate = (1000.0 * n_spikes / simtime) / n_neuron
 
 plt.figure(1)
 plt.plot(times, spikes, "o")
-plt.title(f"Average spike rate (Hz): {hz_rate:.2f}")
+plt.title(f"Average spike rate: {spike_rate:.2f} spks/s")
 plt.xlabel("time (ms)")
 plt.ylabel("neuron no")
 plt.show()

pynest/examples/gif_pop_psc_exp.py

@@ -226,12 +226,12 @@
 plt.figure(1)
 plt.clf()
 plt.subplot(2, 1, 1)
-plt.plot(t, A_N * 1000)  # plot population activities (in Hz)
-plt.ylabel(r"$A_N$ [Hz]")
+plt.plot(t, A_N * 1000)  # plot population activities (in spks/s)
+plt.ylabel(r"$A_N$ [spks/s]")
 plt.title("Population activities (mesoscopic sim.)")
 plt.subplot(2, 1, 2)
 plt.plot(t, Abar * 1000)  # plot instantaneous population rates (in Hz)
-plt.ylabel(r"$\bar A$ [Hz]")
+plt.ylabel(r"$\bar A$ [spks/s]")
 plt.xlabel("time [ms]")
 
 ###############################################################################
@@ -334,19 +334,19 @@
 
 ###############################################################################
 # Let's retrieve the data of the spike recorder and plot the activity of the
-# excitatory population (in Hz):
+# excitatory population (in spks/s):
 
 for i in range(len(nest_pops)):
     data_sr = nest_sr[i].get("events", "times") * dt - t0
     bins = np.concatenate((t, np.array([t[-1] + dt_rec])))
     A = np.histogram(data_sr, bins=bins)[0] / float(N[i]) / dt_rec
-    A_N[:, i] = A * 1000  # in Hz
+    A_N[:, i] = A * 1000  # in spks/s
 
 t = np.arange(dt, t_end + dt, dt_rec)
 plt.figure(2)
 plt.plot(t, A_N[:, 0])
 plt.xlabel("time [ms]")
-plt.ylabel("population activity [Hz]")
+plt.ylabel("population activity [spks/s]")
 plt.title("Population activities (microscopic sim.)")
 
 ###############################################################################

pynest/examples/gif_population.py

@@ -95,7 +95,7 @@
 # GIF neurons population.
 
 N_noise = 50  # size of Poisson group
-rate_noise = 10.0  # firing rate of Poisson neurons (Hz)
+rate_noise = 10.0  # firing rate of Poisson neurons (spks/s)
 w_noise = 20.0  # synaptic weights from Poisson to population neurons (pA)
 
 ###############################################################################

pynest/examples/one_neuron_with_noise.py

@@ -56,9 +56,9 @@
 ###############################################################################
 # Third, the Poisson generator is configured using ``SetStatus``, which expects
 # a list of node handles and a list of parameter dictionaries. We set the
-# Poisson generators to 8,000 Hz and 15,000 Hz, respectively. Note that we do
-# not need to set parameters for the neuron and the voltmeter, since they have
-# satisfactory defaults.
+# Poisson generators to 8,000 spks/s and 15,000 spks/s, respectively. Note that
+# we do not need to set parameters for the neuron and the voltmeter, since they
+# have satisfactory defaults.
 
 noise[0].rate = 80000.0
 noise[1].rate = 15000.0

pynest/examples/pong/networks.py

@@ -259,7 +259,7 @@ class PongNetDopa(PongNet):
     # Synaptic delay for the direct connection between striatum and
     # dopaminergic neurons
     d_dir = 200
-    # Rate (Hz) for the background poisson generators
+    # Rate (spks/s) for the background poisson generators
     poisson_rate = 15
 
     def __init__(self, apply_noise=True, num_neurons=20):

pynest/examples/urbanczik_synapse_example.py

@@ -217,7 +217,7 @@ def h(U, nrn_params):
 # poisson generators. The recorded spike times are then given to spike
 # generators.
 n_pg = 200  # number of poisson generators
-p_rate = 10.0  # rate in Hz
+p_rate = 10.0  # rate in spks/s
 
 pgs = nest.Create("poisson_generator", n=n_pg, params={"rate": p_rate})
 

pynest/nest/raster_plot.py

@@ -268,7 +268,7 @@ def _make_plot(ts, ts1, node_ids, neurons, hist=True, hist_binwidth=5.0, graysca
 
         plt.bar(t_bins, heights, width=hist_binwidth, color=color_bar, edgecolor=color_edge)
         plt.yticks([int(x) for x in numpy.linspace(0.0, int(max(heights) * 1.1) + 5, 4)])
-        plt.ylabel("Rate (Hz)")
+        plt.ylabel("Rate (spks/s)")
         plt.xlabel(xlabel)
         plt.xlim(xlim)
         plt.axes(ax1)

testsuite/pytests/test_jonke_synapse.py

@@ -38,8 +38,8 @@ class TestJonkeSynapse:
     """
 
     resolution = 0.1  # [ms]
-    presynaptic_firing_rate = 20.0  # [Hz]
-    postsynaptic_firing_rate = 20.0  # [Hz]
+    presynaptic_firing_rate = 20.0  # [spks/s]
+    postsynaptic_firing_rate = 20.0  # [spks/s]
     simulation_duration = 1e4  # [ms]
     hardcoded_trains_length = 15.0  # [ms]
     synapse_parameters = {