Repeated Stimulation

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_detector, using the following paradigm:
  1. A single trial last for 1000ms.
  2. Within each trial, the poisson_generator is active from 100ms to 500ms.
We achieve this by defining the 'start' and 'stop' properties of the generator to 100ms and 500ms, 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 nest module is imported for simulation
import nest

Second, the parameters are set 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. The Kernel is reset and a poisson_generator created and the handle is stored in pg
The parameters for rate and start and stop of activity are given as optional parameter in the form of a dictionary
nest.ResetKernel()
pg = nest.Create('poisson_generator',
                 params = {'rate'  : rate,
                           'start' : start,
                           'stop'  : stop}
                 )

The spikedetector is created and the handle stored in sd
sd = nest.Create('spike_detector')

The connect function connects the nodes so spikes from pg are collected by the spike_detector sd
nest.Connect(pg, sd)

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 trail_duration
for n in range(num_trials):
    nest.SetStatus(pg, {'origin': nest.GetKernelStatus()['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 100ms into each trial. This is due to sub-optimal automatic placement of histogram bin borders.
                             title='Repeated stimulation by Poisson generator')
repeated_stimulation
                                # -*- coding: utf-8 -*-
#
# repeated_stimulation.py
#
# This file is part of NEST.
#
# Copyright (C) 2004 The NEST Initiative
#
# NEST is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# NEST is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with NEST.  If not, see <http://www.gnu.org/licenses/>.

'''
Repeated Stimulation 
--------------------

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_detector, using the following paradigm:

1. A single trial last for 1000ms.
2. Within each trial, the poisson_generator is active from 100ms to 500ms.

We achieve this by defining the 'start' and 'stop' properties of the
generator to 100ms and 500ms, 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 nest module is imported for simulation
'''

import nest

'''
Second, the parameters are set 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.  The Kernel is reset and a
poisson_generator created and the handle is stored in pg

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

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

'''
The spikedetector is created and the handle stored in sd
'''

sd = nest.Create('spike_detector')

'''
The connect function connects the nodes so spikes from pg are
collected by the spike_detector sd
'''

nest.Connect(pg, sd)

'''
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 trail_duration
'''

for n in range(num_trials):
    nest.SetStatus(pg, {'origin': nest.GetKernelStatus()['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 100ms into each trial. This is due to
sub-optimal automatic placement of histogram bin borders.
'''

import nest.raster_plot
nest.raster_plot.from_device(sd, hist=True, hist_binwidth=100.,
                             title='Repeated stimulation by Poisson generator')