Sinusoidal poisson generator example

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This script demonstrates the use of the sinusoidal_poisson_generator and its different parameters and modes. The source code of the model can be found in models/sinusoidal_poisson_generator.h.

The script is structured into two parts and creates one common figure. In Part 1, two instances of the sinusoidal_poisson_generator are created with different parameters. Part 2 illustrates the effect of the individual_spike_trains switch.

We import the modules required to simulate, analyze and plot this example.

import matplotlib.pyplot as plt
import nest
import numpy as np

nest.ResetKernel()  # in case we run the script multiple times from iPython

We create two instances of the sinusoidal_poisson_generator with two different parameter sets using Create. Moreover, we create devices to record firing rates (multimeter) and spikes (spike_recorder) and connect them to the generators using Connect.

nest.resolution = 0.01

num_nodes = 2
g = nest.Create(
    "sinusoidal_poisson_generator",
    n=num_nodes,
    params={"rate": [10000.0, 0.0], "amplitude": [5000.0, 10000.0], "frequency": [10.0, 5.0], "phase": [0.0, 90.0]},
)

m = nest.Create("multimeter", num_nodes, {"interval": 0.1, "record_from": ["rate"]})
s = nest.Create("spike_recorder", num_nodes)

nest.Connect(m, g, "one_to_one")
nest.Connect(g, s, "one_to_one")
print(m.get())
nest.Simulate(200)

After simulating, the spikes are extracted from the spike_recorder and plots are created with panels for the PST and ISI histograms.

colors = ["b", "g"]

for j in range(num_nodes):
    ev = m[j].events
    t = ev["times"]
    r = ev["rate"]

    spike_times = s[j].events["times"]
    plt.subplot(221)
    h, e = np.histogram(spike_times, bins=np.arange(0.0, 201.0, 5.0))
    plt.plot(t, r, color=colors[j])
    plt.step(e[:-1], h * 1000 / 5.0, color=colors[j], where="post")
    plt.title("PST histogram and firing rates")
    plt.ylabel("Spikes per second")

    plt.subplot(223)
    plt.hist(np.diff(spike_times), bins=np.arange(0.0, 1.005, 0.02), histtype="step", color=colors[j])
    plt.title("ISI histogram")

The kernel is reset and the number of threads set to 4.

nest.ResetKernel()
nest.local_num_threads = 4

A sinusoidal_poisson_generator with individual_spike_trains set to True is created and connected to 20 parrot neurons whose spikes are recorded by a spike_recorder. After simulating, a raster plot of the spikes is created.

g = nest.Create(
    "sinusoidal_poisson_generator",
    params={"rate": 100.0, "amplitude": 50.0, "frequency": 10.0, "phase": 0.0, "individual_spike_trains": True},
)
p = nest.Create("parrot_neuron", 20)
s = nest.Create("spike_recorder")

nest.Connect(g, p, "all_to_all")
nest.Connect(p, s, "all_to_all")

nest.Simulate(200)
ev = s.events
plt.subplot(222)
plt.plot(ev["times"], ev["senders"] - min(ev["senders"]), "o")
plt.ylim([-0.5, 19.5])
plt.yticks([])
plt.title("Individual spike trains for each target")

The kernel is reset again and the whole procedure is repeated for a sinusoidal_poisson_generator with individual_spike_trains set to False. The plot shows that in this case, all neurons receive the same spike train from the sinusoidal_poisson_generator.

nest.ResetKernel()
nest.local_num_threads = 4

g = nest.Create(
    "sinusoidal_poisson_generator",
    params={"rate": 100.0, "amplitude": 50.0, "frequency": 10.0, "phase": 0.0, "individual_spike_trains": False},
)
p = nest.Create("parrot_neuron", 20)
s = nest.Create("spike_recorder")

nest.Connect(g, p, "all_to_all")
nest.Connect(p, s, "all_to_all")

nest.Simulate(200)
ev = s.events
plt.subplot(224)
plt.plot(ev["times"], ev["senders"] - min(ev["senders"]), "o")
plt.ylim([-0.5, 19.5])
plt.yticks([])
plt.title("One spike train for all targets")
plt.show()