IAF Neuron example

A DC current is injected into the neuron using a current generator device. The membrane potential as well as the spiking activity are recorded by corresponding devices.
It can be observed how the current charges the membrane, a spike is emitted, the neuron becomes absolute refractory, and finally starts to recover.
First, we import all necessary modules for simulation and plotting
import nest
import pylab

Second the Function build_network is defined to build the network and return the handles of the spike detector and the voltmeter
def build_network(dt) :

    nest.ResetKernel()
    nest.SetKernelStatus({"local_num_threads" : 1, "resolution" : dt})

    neuron = nest.Create('iaf_neuron')
    nest.SetStatus(neuron, "I_e", 376.0)

    vm = nest.Create('voltmeter')
    nest.SetStatus(vm, "withtime", True)

    sd = nest.Create('spike_detector')

    nest.Connect(vm, neuron)
    nest.Connect(neuron, sd)

    return vm, sd

The function build_network takes the resolution as argument. First the Kernel is reset and the number of threads is set to zero as well as the resolution to the specified value dt. The iaf_neuron is created and the handle is stored in the variable neuron The status of the neuron is changed so it receives an external current. Next the voltmeter is created and the handle stored in vm and the option 'withtime' is set, therefore times are given in the times vector in events. Now the spike_detecor is created and its handle is stored in sd.
Voltmeter and spikedetector are then connected to the neuron. The connect function takes the handles as input. The Voltmeter is connected to the neuron and the neuron to the spikedetector because the neuron sends spikes to the detector and the voltmeter 'observes' the neuron.
The neuron is simulated for three different resolutions and then the voltage trace is plotted
for dt in [0.1, 0.5, 1.0] :
    print("Running simulation with dt=%.2f" % dt)
    vm, sd = build_network(dt)
    
First using build_network the network is build and the handles of the spike detector and the voltmeter are stored in vm and sd
    
    nest.Simulate(1000.0)

The network is simulated using Simulate, which takes the desired simulation time in milliseconds and advances the network state by this amount of time. During simulation, the spike_detector counts the spikes of the target neuron and the total number is read out at the end of the simulation period.
    
    potentials = nest.GetStatus(vm, "events")[0]["V_m"]
    times = nest.GetStatus(vm, "events")[0]["times"]

The values of the voltage recorded by the voltmeter are read out and the values for the membrane potential are stored in potential and the corresponding times in the times array
    
    pylab.plot(times, potentials, label="dt=%.2f" % dt)
    print("  Number of spikes: {0}".format(nest.GetStatus(sd, "n_events")[0]))

Using the pylab library the voltage trace is plotted over time
    pylab.legend(loc=3)
    pylab.xlabel("time (ms)")
    pylab.ylabel("V_m (mV)")
    
Finally the axis are labelled and a legend is generated
testiaf
                                # -*- coding: utf-8 -*-
#
# testiaf.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/>.

'''
IAF Neuron example
------------------

A DC current is injected into the neuron using a current generator 
device. The membrane potential as well as the spiking activity are 
recorded by corresponding devices.

It can be observed how the current charges the membrane, a spike
is emitted, the neuron becomes absolute refractory, and finally
starts to recover.
'''


'''
First, we import all necessary modules for simulation and plotting
'''

import nest
import pylab

'''
Second the Function build_network is defined to build the network and
return the handles of the spike detector and the voltmeter
'''

def build_network(dt) :

    nest.ResetKernel()
    nest.SetKernelStatus({"local_num_threads" : 1, "resolution" : dt})

    neuron = nest.Create('iaf_neuron')
    nest.SetStatus(neuron, "I_e", 376.0)

    vm = nest.Create('voltmeter')
    nest.SetStatus(vm, "withtime", True)

    sd = nest.Create('spike_detector')

    nest.Connect(vm, neuron)
    nest.Connect(neuron, sd)

    return vm, sd

'''
The function build_network takes the resolution as argument.
First the Kernel is reset and the number of threads is set to zero as
well as the resolution to the specified value dt.  The iaf_neuron is
created and the handle is stored in the variable neuron The status of
the neuron is changed so it receives an external current.  Next the
voltmeter is created and the handle stored in vm and the option
'withtime' is set, therefore times are given in the times vector in
events. Now the spike_detecor is created and its handle is stored in
sd.

Voltmeter and spikedetector are then connected to the neuron.  The
connect function takes the handles as input.  The Voltmeter is
connected to the neuron and the neuron to the spikedetector because
the neuron sends spikes to the detector and the voltmeter 'observes'
the neuron.
'''

'''
The neuron is simulated for three different resolutions and then
the voltage trace is plotted
'''

for dt in [0.1, 0.5, 1.0] :
    print("Running simulation with dt=%.2f" % dt)
    vm, sd = build_network(dt)
    
    '''
    First using build_network the network is build and the handles of
    the spike detector and the voltmeter are stored in vm and sd
    '''
    
    nest.Simulate(1000.0)

    '''
    The network is simulated using `Simulate`, which takes the desired
    simulation time in milliseconds and advances the network state by
    this amount of time. During simulation, the `spike_detector`
    counts the spikes of the target neuron and the total number is
    read out at the end of the simulation period.
    '''
    
    potentials = nest.GetStatus(vm, "events")[0]["V_m"]
    times = nest.GetStatus(vm, "events")[0]["times"]

    '''
    The values of the voltage recorded by the voltmeter are read out
    and the values for the membrane potential are stored in potential
    and the corresponding times in the times array
    '''
    
    pylab.plot(times, potentials, label="dt=%.2f" % dt)
    print("  Number of spikes: {0}".format(nest.GetStatus(sd, "n_events")[0]))

    '''
    Using the pylab library the voltage trace is plotted over time
    '''

    pylab.legend(loc=3)
    pylab.xlabel("time (ms)")
    pylab.ylabel("V_m (mV)")
    
    '''
    Finally the axis are labelled and a legend is generated
    '''