Gap Junctions: Two neuron example

This script simulates two Hodgkin-Huxley neurons of type hh_psc_alpha_gap connected by a gap junction. Both neurons receive a constant current of 100.0 pA. The neurons are initialized with different membrane potentials and synchronize over time due to the gap-junction connection.

import nest
import pylab as pl
import numpy


First we set the resolution of the simulation, create two neurons and create a voltmeter for recording.

nest.SetKernelStatus({'resolution': 0.05})

neuron = nest.Create('hh_psc_alpha_gap', 2)

vm = nest.Create('voltmeter', params={'to_file': False,
                                      'withgid': True,
                                      'withtime': True,
                                      'interval': 0.1})

Then we set the constant current input, modify the inital membrane potential of one of the neurons and connect the neurons to the voltmeter.

nest.SetStatus(neuron, {'I_e': 100.})
nest.SetStatus([neuron[0]], {'V_m': -10.})

nest.Connect(vm, neuron, 'all_to_all')

In order to create the gap_junction connection we employ the all_to_all connection rule: Gap junctions are bidirectional connections, therefore we need to connect neuron[0] to neuron[1] and neuron[1] to neuron[0]:

nest.Connect(neuron, neuron,
             {'rule': 'all_to_all', 'autapses': False},
             {'model': 'gap_junction', 'weight': 0.5})

Finally we start the simulation and plot the membrane potentials of both neurons.


senders = nest.GetStatus(vm, 'events')[0]['senders']
times = nest.GetStatus(vm, 'events')[0]['times']
V = nest.GetStatus(vm, 'events')[0]['V_m']

pl.plot(times[numpy.where(senders == 1)],
        V[numpy.where(senders == 1)], 'r-')
pl.plot(times[numpy.where(senders == 2)],
        V[numpy.where(senders == 2)], 'g-')
pl.xlabel('time (ms)')
pl.ylabel('membrane potential (mV)')