IF curve example

This example illustrates how to measure the I-F curve of a neuron. The program creates a small group of neurons and injects a noisy current I(t) = I_mean + I_std*W(t) where W(t) is a white noise process. The programm systematically drives the current through a series of values in the two-dimensional (I_mean, I_std) space and measures the firing rate of the neurons.
In this example, we measure the I-F curve of the adaptive exponential integrate and fire neuron (aeif_cond_exp), but any other neuron model that accepts current inputs is possible. The model and its parameters are supplied when the IF_curve object is created.
import numpy
import nest
import shelve

Here we define which model and the neuron parameters to use for measuring the transfer function.
model = 'aeif_cond_exp'
params = {'a': 4.0,
          'b': 80.8,
          'V_th': -50.4,
          'Delta_T': 2.0,
          'I_e': 0.0,
          'C_m': 281.0,
          'g_L': 30.0,
          'V_reset': -70.6,
          'tau_w': 144.0,
          't_ref': 5.0,
          'V_peak': -40.0,
          'E_L': -70.6,
          'E_ex': 0.,
          'E_in': -70.}


class IF_curve():
This example illustrates how to measure the I-F curve of a neuron. The program creates a small group of neurons and injects a noisy current I(t)= I_mean + I_std*W(t) where W(t) is a white noise process. The programm systematically drives the current through a series of values in the two-dimensional (I_mean, I_std) space and measures the firing rate of the neurons.
    t_inter_trial = 200.  # Interval between two successive measurement trials
    t_sim = 1000.         # Duration of a measurement trial
    n_neurons = 100       # Number of neurons
    n_threads = 4         # Nubmer of threads to run the simulation

    def __init__(self, model, params=False):
        self.model = model
        self.params = params
        self.build()
        self.connect()

    def build(self):
We reset NEST to delete information from previous simulations and adjust the number of threads.
        nest.ResetKernel()
        nest.SetKernelStatus({'local_num_threads': self.n_threads})
We set the default parameters of the neuron model to those defined above and create neurons and devices.
        if self.params:
            nest.SetDefaults(self.model, self.params)
        self.neuron = nest.Create(self.model, self.n_neurons)
        self.noise = nest.Create('noise_generator')
        self.spike_detector = nest.Create('spike_detector')

    def connect(self):
We connect the noisy current to the neurons and the neurons to the spike detectors.
        nest.Connect(self.noise, self.neuron, 'all_to_all')
        nest.Connect(self.neuron, self.spike_detector, 'all_to_all')

    def output_rate(self, mean, std):
        self.build()
        self.connect()
We adjust the parameters of the noise according to the current values.
        nest.SetStatus(self.noise, [{'mean': mean, 'std': std, 'start': 0.0,
                                     'stop': 1000., 'origin': 0.}])
We simulate the network and calculate the rate.
        nest.Simulate(self.t_sim)
        rate = nest.GetStatus(self.spike_detector, 'n_events')[0] * 1000.0 
            / (1. * self.n_neurons * self.t_sim)
        return rate

    def compute_transfer(self, i_mean=(400.0, 900.0, 50.0),
                         i_std=(0.0, 600.0, 50.0)):
We loop through all possible combinations of (I_mean, I_sigma) and measure the output rate of the neuron.
        self.i_range = numpy.arange(*i_mean)
        self.std_range = numpy.arange(*i_std)
        self.rate = numpy.zeros((self.i_range.size, self.std_range.size))
        nest.set_verbosity('M_WARNING')
        for n, i in enumerate(self.i_range):
            print('I  =  {0}'.format(i))
            for m, std in enumerate(self.std_range):
                self.rate[n, m] = self.output_rate(i, std)

transfer = IF_curve(model, params)
transfer.compute_transfer()
After the simulation is finished we store the data into a file for later analysis.
dat.close()

                                # -*- coding: utf-8 -*-
#
# if_curve.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/>.

'''
IF curve example
----------------

This example illustrates how to measure the I-F curve of a neuron.
The program creates a small group of neurons and injects a noisy current
I(t) = I_mean + I_std*W(t)
where W(t) is a white noise process.
The programm systematically drives the current through a series of
values in the two-dimensional (I_mean, I_std) space and measures the
firing rate of the neurons.

In this example, we measure the I-F curve of the adaptive exponential
integrate and fire neuron (aeif_cond_exp), but any other neuron model
that accepts current inputs is possible. The model and its parameters
are supplied when the IF_curve object is created.
'''

import numpy
import nest
import shelve

'''
Here we define which model and the neuron parameters to use for
measuring the transfer function.
'''
model = 'aeif_cond_exp'
params = {'a': 4.0,
          'b': 80.8,
          'V_th': -50.4,
          'Delta_T': 2.0,
          'I_e': 0.0,
          'C_m': 281.0,
          'g_L': 30.0,
          'V_reset': -70.6,
          'tau_w': 144.0,
          't_ref': 5.0,
          'V_peak': -40.0,
          'E_L': -70.6,
          'E_ex': 0.,
          'E_in': -70.}


class IF_curve():
    '''
    This example illustrates how to measure the I-F curve of a neuron.
    The program creates a small group of neurons and injects a noisy current
    I(t)= I_mean + I_std*W(t)
    where W(t) is a white noise process.
    The programm systematically drives the current through a series of
    values in the two-dimensional (I_mean, I_std) space and measures the
    firing rate of the neurons.
    '''

    t_inter_trial = 200.  # Interval between two successive measurement trials
    t_sim = 1000.         # Duration of a measurement trial
    n_neurons = 100       # Number of neurons
    n_threads = 4         # Nubmer of threads to run the simulation

    def __init__(self, model, params=False):
        self.model = model
        self.params = params
        self.build()
        self.connect()

    def build(self):
        '''
        We reset NEST to delete information from previous simulations
        and adjust the number of threads.
        '''
        nest.ResetKernel()
        nest.SetKernelStatus({'local_num_threads': self.n_threads})
        '''
        We set the default parameters of the neuron model to those
        defined above and create neurons and devices.
        '''
        if self.params:
            nest.SetDefaults(self.model, self.params)
        self.neuron = nest.Create(self.model, self.n_neurons)
        self.noise = nest.Create('noise_generator')
        self.spike_detector = nest.Create('spike_detector')

    def connect(self):
        '''
        We connect the noisy current to the neurons and the neurons to
        the spike detectors.
        '''
        nest.Connect(self.noise, self.neuron, 'all_to_all')
        nest.Connect(self.neuron, self.spike_detector, 'all_to_all')

    def output_rate(self, mean, std):
        self.build()
        self.connect()
        '''
        We adjust the parameters of the noise according to the current values.
        '''
        nest.SetStatus(self.noise, [{'mean': mean, 'std': std, 'start': 0.0,
                                     'stop': 1000., 'origin': 0.}])
        '''
        We simulate the network and calculate the rate.
        '''
        nest.Simulate(self.t_sim)
        rate = nest.GetStatus(self.spike_detector, 'n_events')[0] * 1000.0 
            / (1. * self.n_neurons * self.t_sim)
        return rate

    def compute_transfer(self, i_mean=(400.0, 900.0, 50.0),
                         i_std=(0.0, 600.0, 50.0)):
        '''
        We loop through all possible combinations of (I_mean, I_sigma)
        and measure the output rate of the neuron.
        '''
        self.i_range = numpy.arange(*i_mean)
        self.std_range = numpy.arange(*i_std)
        self.rate = numpy.zeros((self.i_range.size, self.std_range.size))
        nest.set_verbosity('M_WARNING')
        for n, i in enumerate(self.i_range):
            print('I  =  {0}'.format(i))
            for m, std in enumerate(self.std_range):
                self.rate[n, m] = self.output_rate(i, std)

transfer = IF_curve(model, params)
transfer.compute_transfer()
'''
After the simulation is finished we store the data into a file for
later analysis.
'''
dat = shelve.open(model+'_transfer.dat')
dat['I_mean'] = transfer.i_range
dat['I_std'] = transfer.std_range
dat['rate'] = transfer.rate
dat.close()