Note
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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 pylab
import numpy
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.ResetKernel()
nest.SetKernelStatus({"resolution": dt, "use_wfr": False,
"print_time": True,
"overwrite_files": True})
print("Building network")
Configuration of the neuron model using SetDefaults
.
nest.SetDefaults(neuron_model, neuron_params)
Creation of the nodes using Create
.
n_e = nest.Create(neuron_model, NE)
n_i = nest.Create(neuron_model, NI)
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 associatated
with a delay d (rate_connection_delayed
).
Connections originating from inhibitory neurons are not associatated
with a delay (rate_connection_instantaneous
).
syn_e = {'weight': w, 'delay': d_e, 'model': 'rate_connection_delayed'}
syn_i = {'weight': -g*w, '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
nest.Simulate(T)
Plot rates of one excitatory and one inhibitory neuron
data = nest.GetStatus(mm)[0]['events']
rate_ex = data['rate'][numpy.where(data['senders'] == n_e[0])]
rate_in = data['rate'][numpy.where(data['senders'] == n_i[0])]
times = data['times'][numpy.where(data['senders'] == n_e[0])]
pylab.figure()
pylab.plot(times, rate_ex, label='excitatory')
pylab.plot(times, rate_in, label='inhibitory')
pylab.xlabel('time (ms)')
pylab.ylabel('rate (a.u.)')
pylab.show()
Total running time of the script: ( 0 minutes 0.000 seconds)