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Sinusoidal poisson generator exampleΒΆ
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()
Total running time of the script: ( 0 minutes 0.000 seconds)