Network of linear rate neuronsΒΆ

This script simulates an excitatory and an inhibitory population of lin_rate_ipn neurons with delayed excitatory and instantaneous inhibitory connections. The rate of all neurons is recorded using a multimeter. The resulting rate for one excitatory and one inhibitory neuron is plotted.

import nest
import numpy
import matplotlib.pyplot as plt

Assigning the simulation parameters to variables.

dt = 0.1  # the resolution in ms
T = 100.0  # Simulation time in ms

Definition of the number of neurons

order = 50
NE = int(4 * order)    # number of excitatory neurons
NI = int(1 * order)    # number of inhibitory neurons
N = int(NE + NI)       # total number of neurons

Definition of the connections

d_e = 5.   # delay of excitatory connections in ms
g = 5.0  # ratio inhibitory weight/excitatory weight
epsilon = 0.1  # connection probability
w = 0.1 / numpy.sqrt(N)  # excitatory connection strength

KE = int(epsilon * NE)  # number of excitatory synapses per neuron (outdegree)
KI = int(epsilon * NI)  # number of inhibitory synapses per neuron (outdegree)
K_tot = int(KI + KE)  # total number of synapses per neuron
connection_rule = 'fixed_outdegree'  # connection rule

Definition of the neuron model and its neuron parameters

neuron_model = 'lin_rate_ipn'  # neuron model
neuron_params = {'linear_summation': True,
                 # type of non-linearity (not affecting linear rate models)
                 'tau': 10.0,
                 # time constant of neuronal dynamics in ms
                 'mu': 2.0,
                 # mean input
                 'sigma': 5.
                 # noise parameter

Configuration of the simulation kernel by the previously defined time resolution used in the simulation. Setting print_time to True prints the already processed simulation time as well as its percentage of the total simulation time.

nest.resolution = dt
nest.use_wfr = False
nest.print_time = True
nest.overwrite_files = True

print("Building network")

Creation of the nodes using Create.

n_e = nest.Create(neuron_model, NE, neuron_params)
n_i = nest.Create(neuron_model, NI, neuron_params)

To record from the rate neurons a multimeter is created and the parameter record_from is set to rate as well as the recording interval to dt

mm = nest.Create('multimeter', params={'record_from': ['rate'],
                                       'interval': dt})

Specify synapse and connection dictionaries: Connections originating from excitatory neurons are associated with a delay d (rate_connection_delayed). Connections originating from inhibitory neurons are not associated with a delay (rate_connection_instantaneous).

syn_e = {'weight': w, 'delay': d_e, 'synapse_model': 'rate_connection_delayed'}
syn_i = {'weight': -g * w, 'synapse_model': 'rate_connection_instantaneous'}
conn_e = {'rule': connection_rule, 'outdegree': KE}
conn_i = {'rule': connection_rule, 'outdegree': KI}

Connect rate units

nest.Connect(n_e, n_e, conn_e, syn_e)
nest.Connect(n_i, n_i, conn_i, syn_i)
nest.Connect(n_e, n_i, conn_i, syn_e)
nest.Connect(n_i, n_e, conn_e, syn_i)

Connect recording device to rate units

nest.Connect(mm, n_e + n_i)

Simulate the network


Plot rates of one excitatory and one inhibitory neuron

data =
senders = data['senders']
rate = data['rate']
times = data['times']

ne_0_id = n_e[0].global_id
ni_0_id = n_i[0].global_id
where_sender_is_ne_0 = numpy.where(senders == ne_0_id)
where_sender_is_ni_0 = numpy.where(senders == ni_0_id)

rate_ex = rate[where_sender_is_ne_0]
rate_in = rate[where_sender_is_ni_0]
times = times[where_sender_is_ne_0]

plt.plot(times, rate_ex, label='excitatory')
plt.plot(times, rate_in, label='inhibitory')
plt.xlabel('time (ms)')
plt.ylabel('rate (a.u.)')

Total running time of the script: ( 0 minutes 0.000 seconds)

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