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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 matplotlib.pyplot as plt
import numpy

nest.ResetKernel()

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

nest.resolution = 0.05

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

vm = nest.Create('voltmeter', params={'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.

neuron.I_e = 100.
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', 'allow_autapses': False},
             {'synapse_model': 'gap_junction', 'weight': 0.5})

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

nest.Simulate(351.)

senders = vm.events['senders']
times = vm.events['times']
v_m_values = vm.events['V_m']

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