Sinusoidal poisson generator example

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 nest
import matplotlib.pyplot as plt
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., 201., 5.))
    plt.plot(t, r, color=colors[j])
    plt.step(e[:-1], h * 1000 / 5., 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., 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()