Gap Junctions: Inhibitory network example

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This script simulates an inhibitory network of 500 Hodgkin-Huxley neurons. Without the gap junctions (meaning for gap_weight = 0.0) the network shows an asynchronous irregular state that is caused by the external excitatory Poissonian drive being balanced by the inhibitory feedback within the network. With increasing gap_weight the network synchronizes:

For a lower gap weight of 0.3 nS the network remains in an asynchronous state. With a weight of 0.54 nS the network switches randomly between the asynchronous to the synchronous state, while for a gap weight of 0.7 nS a stable synchronous state is reached.

This example is also used as test case 2 (see Figure 9 and 10) in [1].


import matplotlib.pyplot as plt
import nest
import numpy

n_neuron = 500
gap_per_neuron = 60
inh_per_neuron = 50
delay = 1.0
j_exc = 300.0
j_inh = -50.0
threads = 8
stepsize = 0.05
simtime = 501.0
gap_weight = 0.3


First we set the random seed, adjust the kernel settings and create hh_psc_alpha_gap neurons, spike_recorder and poisson_generator.


nest.resolution = 0.05
nest.total_num_virtual_procs = threads
nest.print_time = True

# Settings for waveform relaxation. If 'use_wfr' is set to False,
# communication takes place in every step instead of using an
# iterative solution
nest.use_wfr = True
nest.wfr_comm_interval = 1.0
nest.wfr_tol = 0.0001
nest.wfr_max_iterations = 15
nest.wfr_interpolation_order = 3

neurons = nest.Create("hh_psc_alpha_gap", n_neuron)

sr = nest.Create("spike_recorder")
pg = nest.Create("poisson_generator", params={"rate": 500.0})

Each neuron shall receive inh_per_neuron = 50 inhibitory synaptic inputs that are randomly selected from all other neurons, each with synaptic weight j_inh = -50.0 pA and a synaptic delay of 1.0 ms. Furthermore each neuron shall receive an excitatory external Poissonian input of 500.0 Hz with synaptic weight j_exc = 300.0 pA and the same delay. The desired connections are created with the following commands:

conn_dict = {"rule": "fixed_indegree", "indegree": inh_per_neuron, "allow_autapses": False, "allow_multapses": True}

syn_dict = {"synapse_model": "static_synapse", "weight": j_inh, "delay": delay}

nest.Connect(neurons, neurons, conn_dict, syn_dict)

nest.Connect(pg, neurons, "all_to_all", syn_spec={"synapse_model": "static_synapse", "weight": j_exc, "delay": delay})

Then the neurons are connected to the spike_recorder and the initial membrane potential of each neuron is set randomly between -40 and -80 mV.

nest.Connect(neurons, sr)

neurons.V_m = nest.random.uniform(min=-80.0, max=-40.0)

Finally gap junctions are added to the network. \((60*500)/2\) gap_junction connections are added randomly resulting in an average of 60 gap-junction connections per neuron. We must not use the fixed_indegree oder fixed_outdegree functionality of nest.Connect() to create the connections, as gap_junction connections are bidirectional connections and we need to make sure that the same neurons are connected in both ways. This is achieved by creating the connections on the Python level with the random module of the Python Standard Library and connecting the neurons using the make_symmetric flag for one_to_one connections.

n_connection = int(n_neuron * gap_per_neuron / 2)
neuron_list = neurons.tolist()
connections = numpy.random.choice(neuron_list, [n_connection, 2])

for source_node_id, target_node_id in connections:
        {"rule": "one_to_one", "make_symmetric": True},
        {"synapse_model": "gap_junction", "weight": gap_weight},

In the end we start the simulation and plot the spike pattern.


events =
times = events["times"]
spikes = events["senders"]
n_spikes = sr.n_events

hz_rate = (1000.0 * n_spikes / simtime) / n_neuron

plt.plot(times, spikes, "o")
plt.title(f"Average spike rate (Hz): {hz_rate:.2f}")
plt.xlabel("time (ms)")
plt.ylabel("neuron no")

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