Spike synchronization through subthreshold oscillation

This script reproduces the spike synchronization behavior of integrate-and-fire neurons in response to a subthreshold oscillation. This phenomenon is shown in Fig. 1 of
C.D. Brody and J.J. Hopfield Simple Networks for Spike-Timing-Based Computation, with Application to Olfactory Processing Neuron 37, 843-852 (2003)
Neurons receive a weak 35 Hz oscillation, a gaussian noise current and an increasing DC. The time-locking capability is shown to depend on the input current given. The result is then plotted using pylab. All parameters are taken from the above paper.
First, we import all necessary modules for simulation, analysis and plotting.
import nest
import nest.raster_plot

Second, the simulation parameters are assigned to variables.
N = 1000           # number of neurons
bias_begin = 140.  # minimal value for the bias current injection [pA]
bias_end = 200.    # maximal value for the bias current injection [pA]
T = 600            # simulation time (ms)

driveparams  = {'amplitude':50., 'frequency':35.}   #parameters for the alternative-current generator
noiseparams  = {'mean':0.0, 'std':200.}             #parameters for the noise generator
neuronparams = { 'tau_m':20.,   #membrane time constant
                 'V_th':20.,    #threshold potential
                 'E_L':10.,     #membrane resting potential
                 't_ref':2.,    #refractory period
                 'V_reset':0.,  #reset potential
                 'C_m':200.,    #membrane capacitance
                 'V_m':0.}      #initial membrane potential

Third, the nodes are created using Create. We store the returned handles in variables for later reference.
neurons = nest.Create('iaf_psc_alpha',N)
sd      = nest.Create('spike_detector')
noise   = nest.Create('noise_generator')
drive   = nest.Create('ac_generator')

Set the parameters specified above for the generators using SetStatus.
nest.SetStatus(drive,   driveparams )
nest.SetStatus(noise,   noiseparams )

Set the parameters specified above for the neurons. Nurons getan internal current. The first neuron additionally receives the current with amplitude bias_begin, the last neuron with amplitude bias_end.
nest.SetStatus(neurons, neuronparams)
nest.SetStatus(neurons, [{'I_e': (n * (bias_end - bias_begin) / N + bias_begin)} for n in neurons])

Set the parameters for the spike_detector: recorded data should include the information about global IDs of spiking neurons and the time of individual spikes.
nest.SetStatus(sd, {"withgid": True, "withtime": True})

Connect alternative current and noise generators as well as spike_detectors. to neurons
nest.DivergentConnect(drive, neurons)
nest.DivergentConnect(noise, neurons)
nest.ConvergentConnect(neurons, sd)

Simulate the network for time T.
nest.Simulate(T)

Plot the raster plot of the neuronal spiking activity.
nest.raster_plot.from_device(sd, hist=True)
BrodyHopfield
                                # -*- coding: utf-8 -*-
#
# BrodyHopfield.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/>.

'''
Spike synchronization through subthreshold oscillation
------------------------------------------------------

This script reproduces the spike synchronization behavior
of integrate-and-fire neurons in response to a subthreshold
oscillation. This phenomenon is shown in Fig. 1 of

  C.D. Brody and J.J. Hopfield
  Simple Networks for Spike-Timing-Based Computation,
  with Application to Olfactory Processing
  Neuron 37, 843-852 (2003)

Neurons receive a weak 35 Hz oscillation, a gaussian noise current
and an increasing DC. The time-locking capability is shown to
depend on the input current given. The result is then plotted using
pylab. All parameters are taken from the above paper.
'''

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

import nest
import nest.raster_plot

'''
Second, the simulation parameters are assigned to variables.
'''

N = 1000           # number of neurons
bias_begin = 140.  # minimal value for the bias current injection [pA]
bias_end = 200.    # maximal value for the bias current injection [pA]
T = 600            # simulation time (ms)

driveparams  = {'amplitude':50., 'frequency':35.}   #parameters for the alternative-current generator
noiseparams  = {'mean':0.0, 'std':200.}             #parameters for the noise generator
neuronparams = { 'tau_m':20.,   #membrane time constant
                 'V_th':20.,    #threshold potential
                 'E_L':10.,     #membrane resting potential
                 't_ref':2.,    #refractory period
                 'V_reset':0.,  #reset potential
                 'C_m':200.,    #membrane capacitance
                 'V_m':0.}      #initial membrane potential

'''
Third, the nodes are created using `Create`. We store the returned
handles in variables for later reference.
'''

neurons = nest.Create('iaf_psc_alpha',N)
sd      = nest.Create('spike_detector')
noise   = nest.Create('noise_generator')
drive   = nest.Create('ac_generator')

'''
Set the parameters specified above for the generators using `SetStatus`.
'''

nest.SetStatus(drive,   driveparams )
nest.SetStatus(noise,   noiseparams )

'''
Set the parameters specified above for the neurons. Nurons getan internal
current. The first neuron additionally receives the current with amplitude
``bias_begin``, the last neuron with amplitude ``bias_end``.
'''

nest.SetStatus(neurons, neuronparams)
nest.SetStatus(neurons, [{'I_e': (n * (bias_end - bias_begin) / N + bias_begin)} for n in neurons])

'''
Set the parameters for the `spike_detector`: recorded data should include
the information about global IDs of spiking neurons and the time of
individual spikes.
'''

nest.SetStatus(sd, {"withgid": True, "withtime": True})

'''
Connect alternative current and noise generators as well as `spike_detector`s.
to neurons
'''

nest.DivergentConnect(drive, neurons)
nest.DivergentConnect(noise, neurons)
nest.ConvergentConnect(neurons, sd)

'''
Simulate the network for time T.
'''

nest.Simulate(T)

'''
Plot the raster plot of the neuronal spiking activity.
'''

nest.raster_plot.from_device(sd, hist=True)