Random balanced network (alpha synapses) connected with NumPy

This script simulates an excitatory and an inhibitory population on the basis of the network used in 1.

In contrast to brunel_alpha_nest.py, this variant uses NumPy to draw the random connections instead of NEST’s builtin connection routines.

When connecting the network customary synapse models are used, which allow for querying the number of created synapses. Using spike detectors the average firing rates of the neurons in the populations are established. The building as well as the simulation time of the network are recorded.



Brunel N (2000). Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. Journal of Computational Neuroscience 8, 183-208.

See Also

Random balanced network (alpha synapses) connected with NEST

Import all necessary modules for simulation, analysis and plotting. Scipy should be imported before nest.

from scipy.optimize import fsolve

import nest
import nest.raster_plot

import numpy
from numpy import exp

import time

Definition of functions used in this example. First, define the Lambert W function implemented in SLI. The second function computes the maximum of the postsynaptic potential for a synaptic input current of unit amplitude ( 1 pA) using the Lambert W function. Thus function will later be used to calibrate the synaptic weights

def LambertWm1(x):
    y = nest.ll_api.sli_pop()
    return y

def ComputePSPnorm(tauMem, CMem, tauSyn):
    a = (tauMem / tauSyn)
    b = (1.0 / tauSyn - 1.0 / tauMem)

    # time of maximum
    t_max = 1.0 / b * (-LambertWm1(-exp(-1.0 / a) / a) - 1.0 / a)

    # maximum of PSP for current of unit amplitude
    return (exp(1.0) / (tauSyn * CMem * b) *
            ((exp(-t_max / tauMem) - exp(-t_max / tauSyn)) / b -
             t_max * exp(-t_max / tauSyn)))


Assigning the current time to a variable in order to determine the build time of the network.

startbuild = time.time()

Assigning the simulation parameters to variables.

dt = 0.1    # the resolution in ms
simtime = 1000.0  # Simulation time in ms
delay = 1.5    # synaptic delay in ms

Definition of the parameters crucial for asynchronous irregular firing of the neurons.

g = 5.0  # ratio inhibitory weight/excitatory weight
eta = 2.0  # external rate relative to threshold rate
epsilon = 0.1  # connection probability

Definition of the number of neurons in the network and the number of neuron recorded from

order = 2500
NE = 4 * order  # number of excitatory neurons
NI = 1 * order  # number of inhibitory neurons
N_neurons = NE + NI   # number of neurons in total
N_rec = 50      # record from 50 neurons

Definition of connectivity parameter

CE = int(epsilon * NE)  # number of excitatory synapses per neuron
CI = int(epsilon * NI)  # number of inhibitory synapses per neuron
C_tot = int(CI + CE)      # total number of synapses per neuron

Initialization of the parameters of the integrate and fire neuron and the synapses. The parameter of the neuron are stored in a dictionary. The synaptic currents are normalized such that the amplitude of the PSP is J.

tauSyn = 0.5  # synaptic time constant in ms
tauMem = 20.0  # time constant of membrane potential in ms
CMem = 250.0  # capacitance of membrane in in pF
theta = 20.0  # membrane threshold potential in mV
neuron_params = {"C_m": CMem,
                 "tau_m": tauMem,
                 "tau_syn_ex": tauSyn,
                 "tau_syn_in": tauSyn,
                 "t_ref": 2.0,
                 "E_L": 0.0,
                 "V_reset": 0.0,
                 "V_m": 0.0,
                 "V_th": theta}
J = 0.1        # postsynaptic amplitude in mV
J_unit = ComputePSPnorm(tauMem, CMem, tauSyn)
J_ex = J / J_unit  # amplitude of excitatory postsynaptic current
J_in = -g * J_ex    # amplitude of inhibitory postsynaptic current

Definition of threshold rate, which is the external rate needed to fix the membrane potential around its threshold, the external firing rate and the rate of the poisson generator which is multiplied by the in-degree CE and converted to Hz by multiplication by 1000.

nu_th = (theta * CMem) / (J_ex * CE * numpy.exp(1) * tauMem * tauSyn)
nu_ex = eta * nu_th
p_rate = 1000.0 * nu_ex * CE

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.SetKernelStatus({"resolution": dt, "print_time": True,
                      "overwrite_files": True})

print("Building network")

Configuration of the model iaf_psc_alpha and poisson_generator using SetDefaults(). This function expects the model to be the inserted as a string and the parameter to be specified in a dictionary. All instances of theses models created after this point will have the properties specified in the dictionary by default.

nest.SetDefaults("iaf_psc_alpha", neuron_params)
nest.SetDefaults("poisson_generator", {"rate": p_rate})

Creation of the nodes using Create. We store the returned handles in variables for later reference. Here the excitatory and inhibitory, as well as the poisson generator and two spike detectors. The spike detectors will later be used to record excitatory and inhibitory spikes.

nodes_ex = nest.Create("iaf_psc_alpha", NE)
nodes_in = nest.Create("iaf_psc_alpha", NI)
noise = nest.Create("poisson_generator")
espikes = nest.Create("spike_detector")
ispikes = nest.Create("spike_detector")

Configuration of the spike detectors recording excitatory and inhibitory spikes using SetStatus, which expects a list of node handles and a list of parameter dictionaries. Setting the variable “to_file” to True ensures that the spikes will be recorded in a .gdf file starting with the string assigned to label. Setting “withtime” and “withgid” to True ensures that each spike is saved to file by stating the gid of the spiking neuron and the spike time in one line.

nest.SetStatus(espikes, [{"label": "brunel-py-ex",
                          "withtime": True,
                          "withgid": True,
                          "to_file": True}])

nest.SetStatus(ispikes, [{"label": "brunel-py-in",
                          "withtime": True,
                          "withgid": True,
                          "to_file": True}])

print("Connecting devices")

Definition of a synapse using CopyModel, which expects the model name of a pre-defined synapse, the name of the customary synapse and an optional parameter dictionary. The parameters defined in the dictionary will be the default parameter for the customary synapse. Here we define one synapse for the excitatory and one for the inhibitory connections giving the previously defined weights and equal delays.

nest.CopyModel("static_synapse", "excitatory",
               {"weight": J_ex, "delay": delay})
nest.CopyModel("static_synapse", "inhibitory",
               {"weight": J_in, "delay": delay})

Connecting the previously defined poisson generator to the excitatory and inhibitory neurons using the excitatory synapse. Since the poisson generator is connected to all neurons in the population the default rule ( ‘all_to_all’) of Connect() is used. The synaptic properties are inserted via syn_spec which expects a dictionary when defining multiple variables or a string when simply using a pre-defined synapse.

nest.Connect(noise, nodes_ex, syn_spec="excitatory")
nest.Connect(noise, nodes_in, syn_spec="excitatory")

Connecting the first N_rec nodes of the excitatory and inhibitory population to the associated spike detectors using excitatory synapses. Here the same shortcut for the specification of the synapse as defined above is used.

nest.Connect(nodes_ex[:N_rec], espikes, syn_spec="excitatory")
nest.Connect(nodes_in[:N_rec], ispikes, syn_spec="excitatory")

print("Connecting network")

Here, we create the connections from the excitatory neurons to all other neurons. We exploit that the neurons have consecutive IDs, running from 1, …,NE for the excitatory neurons and from (NE+1),…,(NE+NI) for the inhibitory neurons.


sources_ex = numpy.random.randint(1, NE + 1, (N_neurons, CE))
sources_in = numpy.random.randint(NE + 1, N_neurons + 1, (N_neurons, CI))

We now iterate over all neuron IDs, and connect the neuron to the sources from our array. The first loop connects the excitatory neurons and the second loop the inhibitory neurons.

for n in range(N_neurons):
    nest.Connect(list(sources_ex[n]), [n + 1], syn_spec="excitatory")

for n in range(N_neurons):
    nest.Connect(list(sources_in[n]), [n + 1], syn_spec="inhibitory")

Storage of the time point after the buildup of the network in a variable.

endbuild = time.time()

Simulation of the network.



Storage of the time point after the simulation of the network in a variable.

endsimulate = time.time()

Reading out the total number of spikes received from the spike detector connected to the excitatory population and the inhibitory population.

events_ex = nest.GetStatus(espikes, "n_events")[0]
events_in = nest.GetStatus(ispikes, "n_events")[0]

Calculation of the average firing rate of the excitatory and the inhibitory neurons by dividing the total number of recorded spikes by the number of neurons recorded from and the simulation time. The multiplication by 1000.0 converts the unit 1/ms to 1/s=Hz.

rate_ex = events_ex / simtime * 1000.0 / N_rec
rate_in = events_in / simtime * 1000.0 / N_rec

Reading out the number of connections established using the excitatory and inhibitory synapse model. The numbers are summed up resulting in the total number of synapses.

num_synapses = (nest.GetDefaults("excitatory")["num_connections"] +

Establishing the time it took to build and simulate the network by taking the difference of the pre-defined time variables.

build_time = endbuild - startbuild
sim_time = endsimulate - endbuild

Printing the network properties, firing rates and building times.

print("Brunel network simulation (Python)")
print("Number of neurons : {0}".format(N_neurons))
print("Number of synapses: {0}".format(num_synapses))
print("       Exitatory  : {0}".format(int(CE * N_neurons) + N_neurons))
print("       Inhibitory : {0}".format(int(CI * N_neurons)))
print("Excitatory rate   : %.2f Hz" % rate_ex)
print("Inhibitory rate   : %.2f Hz" % rate_in)
print("Building time     : %.2f s" % build_time)
print("Simulation time   : %.2f s" % sim_time)

Plot a raster of the excitatory neurons and a histogram.

nest.raster_plot.from_device(espikes, hist=True)

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

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