Repeated Stimulation

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Simple example for how to repeat a stimulation protocol using the origin property of devices.

In this example, a poisson_generator generates a spike train that is recorded directly by a spike_recorder, using the following paradigm:

1. A single trial last for 1000 ms.

2. Within each trial, the poisson_generator is active from 100 ms to 500 ms.

We achieve this by defining the start and stop properties of the generator to 100 ms and 500 ms, respectively, and setting the origin to the simulation time at the beginning of each trial. Start and stop are interpreted relative to the origin.

First, the modules needed for simulation and analysis are imported.

import matplotlib.pyplot as plt
import nest
import nest.raster_plot

Second, we set the parameters so the poisson_generator generates 1000 spikes per second and is active from 100 to 500 ms

rate = 1000.0  # generator rate in spikes/s
start = 100.0  # start of simulation relative to trial start, in ms
stop = 500.0  # end of simulation relative to trial start, in ms

The simulation is supposed to take 1s (1000 ms) and is repeated 5 times

trial_duration = 1000.0  # trial duration, in ms
num_trials = 5  # number of trials to perform

Third, the network is set up. We reset the kernel and create a poisson_generator, in which the handle is stored in pg.

The parameters for rate and start and stop of activity are given as optional parameters in the form of a dictionary.

nest.ResetKernel()
pg_params = {"rate": rate, "start": start, "stop": stop}
pg = nest.Create("poisson_generator", params=pg_params)

The spike_recorder is created and the handle stored in sr.

sr = nest.Create("spike_recorder")

The Connect function connects the nodes so spikes from pg are collected by the spike_recorder sr

nest.Connect(pg, sr)

Before each trial, we set the origin of the poisson_generator to the current simulation time. This automatically sets the start and stop time of the poisson_generator to the specified times with respect to the origin. The simulation is then carried out for the specified time in trial_duration.

for n in range(num_trials):
    pg.origin = nest.biological_time
    nest.Simulate(trial_duration)

Now we plot the result, including a histogram using the nest.raster_plot function. Note: The histogram will show spikes seemingly located before 100 ms into each trial. This is due to sub-optimal automatic placement of histogram bin borders.

nest.raster_plot.from_device(sr, hist=True, hist_binwidth=100.0, title="Repeated stimulation by Poisson generator")
plt.show()