Add simulations to reproduce figure 8 of Brunel (2000)

examples/frompapers/Brunel_2000.py

@@ -0,0 +1,154 @@
+#!/usr/bin/env python3
+"""
+Fig. 8 from:
+
+Brunel, N. Dynamics of Sparsely Connected Networks of Excitatory and
+Inhibitory Spiking Neurons. J Comput Neurosci 8, 183–208
+(2000). https://doi.org/10.1023/A:1008925309027
+
+Inspired by http://neuronaldynamics.epfl.ch
+
+Sebastian Schmitt, 2022
+"""
+
+import random
+from brian2 import *
+import matplotlib.pyplot as plt
+
+
+def sim(g, nu_ext_over_nu_thr, sim_time, ax_spikes, ax_rates, rate_tick_step):
+    """
+    g -- relative inhibitory to excitatory synaptic strength
+    nu_ext_over_nu_thr -- ratio of external stimulus rate to threshold rate
+    sim_time -- simulation time
+    ax_spikes -- matplotlib axes to plot spikes on
+    ax_rates -- matplotlib axes to plot rates on
+    rate_tick_step -- step size for rate axis ticks
+    """
+
+    # network parameters
+    N_E = 10000
+    gamma = 0.25
+    N_I = round(gamma * N_E)
+    N = N_E + N_I
+    epsilon = 0.1
+    C_E = epsilon * N_E
+    C_ext = C_E
+
+    # neuron parameters
+    tau = 20 * ms
+    theta = 20 * mV
+    V_r = 10 * mV
+    tau_rp = 2 * ms
+
+    # synapse parameters
+    J = 0.1 * mV
+    D = 1.5 * ms
+
+    # external stimulus
+    nu_thr = theta / (J * C_E * tau)
+
+    defaultclock.dt = 0.1 * ms
+
+    neurons = NeuronGroup(N,
+                          """
+                          dv/dt = -v/tau : volt (unless refractory)
+                          """,
+                          threshold="v > theta",
+                          reset="v = V_r",
+                          refractory=tau_rp,
+                          method="exact",
+    )
+
+    excitatory_neurons = neurons[:N_E]
+    inhibitory_neurons = neurons[N_E:]
+
+    exc_synapses = Synapses(excitatory_neurons, target=neurons, on_pre="v += J", delay=D)
+    exc_synapses.connect(p=epsilon)
+
+    inhib_synapses = Synapses(inhibitory_neurons, target=neurons, on_pre="v += -g*J", delay=D)
+    inhib_synapses.connect(p=epsilon)
+
+    nu_ext = nu_ext_over_nu_thr * nu_thr
+
+    external_poisson_input = PoissonInput(
+        target=neurons, target_var="v", N=C_ext, rate=nu_ext, weight=J
+    )
+
+    rate_monitor = PopulationRateMonitor(neurons)
+
+    # record from the first 50 excitatory neurons
+    spike_monitor = SpikeMonitor(neurons[:50])
+
+    run(sim_time, report='text')
+
+    ax_spikes.plot(spike_monitor.t / ms, spike_monitor.i, "|")
+    ax_rates.plot(rate_monitor.t / ms, rate_monitor.rate / Hz)
+
+    ax_spikes.set_yticks([])
+
+    ax_spikes.set_xlim(*params["t_range"])
+    ax_rates.set_xlim(*params["t_range"])
+
+    ax_rates.set_ylim(*params["rate_range"])
+    ax_rates.set_xlabel("t [ms]")
+
+    ax_rates.set_yticks(
+        np.arange(
+            params["rate_range"][0], params["rate_range"][1] + rate_tick_step, rate_tick_step
+        )
+    )
+
+    plt.subplots_adjust(hspace=0)
+
+
+parameters = {
+    "A": {
+        "g": 3,
+        "nu_ext_over_nu_thr": 2,
+        "t_range": [500, 600],
+        "rate_range": [0, 6000],
+        "rate_tick_step": 1000,
+    },
+    "B": {
+        "g": 6,
+        "nu_ext_over_nu_thr": 4,
+        "t_range": [1000, 1200],
+        "rate_range": [0, 400],
+        "rate_tick_step": 100,
+    },
+    "C": {
+        "g": 5,
+        "nu_ext_over_nu_thr": 2,
+        "t_range": [1000, 1200],
+        "rate_range": [0, 200],
+        "rate_tick_step": 50,
+    },
+    "D": {
+        "g": 4.5,
+        "nu_ext_over_nu_thr": 0.9,
+        "t_range": [1000, 1200],
+        "rate_range": [0, 250],
+        "rate_tick_step": 50,
+    },
+}
+
+for panel, params in parameters.items():
+
+    fig = plt.figure(figsize=(4, 5))
+    fig.suptitle(panel)
+
+    gs = fig.add_gridspec(ncols=1, nrows=2, height_ratios=[4, 1])
+
+    ax_spikes, ax_rates = gs.subplots(sharex="col")
+
+    sim(
+        params["g"],
+        params["nu_ext_over_nu_thr"],
+        params["t_range"][1] * ms,
+        ax_spikes,
+        ax_rates,
+        params["rate_tick_step"],
+    )
+
+plt.show()