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Sinusoidal poisson generator exampleΒΆ
Run this example as a Jupyter notebook:
See our guide for more information and troubleshooting.
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()