## IAF Neuron example

A DC current is injected into the neuron using a current generator device. The membrane potential as well as the spiking activity are recorded by corresponding devices.

It can be observed how the current charges the membrane, a spike is emitted, the neuron becomes absolute refractory, and finally starts to recover.

First, we import all necessary modules for simulation and plotting

import nest
import pylab

Second the Function build_network is defined to build the network and return the handles of the spike detector and the voltmeter

def build_network(dt):

nest.ResetKernel()

neuron = nest.Create('iaf_psc_alpha')
nest.SetStatus(neuron, "I_e", 376.0)

vm = nest.Create('voltmeter')
nest.SetStatus(vm, "withtime", True)

sd = nest.Create('spike_detector')

nest.Connect(vm, neuron)
nest.Connect(neuron, sd)

return vm, sd

The function build_network takes the resolution as argument. First the Kernel is reset and the number of threads is set to zero as well as the resolution to the specified value dt. The iaf_psc_alpha is created and the handle is stored in the variable neuron The status of the neuron is changed so it receives an external current. Next the voltmeter is created and the handle stored in vm and the option 'withtime' is set, therefore times are given in the times vector in events. Now the spike_detecor is created and its handle is stored in sd.

Voltmeter and spikedetector are then connected to the neuron. The connect function takes the handles as input. The Voltmeter is connected to the neuron and the neuron to the spikedetector because the neuron sends spikes to the detector and the voltmeter 'observes' the neuron.

The neuron is simulated for three different resolutions and then the voltage trace is plotted

for dt in [0.1, 0.5, 1.0]:
print("Running simulation with dt=%.2f" % dt)
vm, sd = build_network(dt)

First using build_network the network is build and the handles of the spike detector and the voltmeter are stored in vm and sd

    nest.Simulate(1000.0)

The network is simulated using Simulate, which takes the desired simulation time in milliseconds and advances the network state by this amount of time. During simulation, the spike_detector counts the spikes of the target neuron and the total number is read out at the end of the simulation period.

    potentials = nest.GetStatus(vm, "events")[0]["V_m"]
times = nest.GetStatus(vm, "events")[0]["times"]

The values of the voltage recorded by the voltmeter are read out and the values for the membrane potential are stored in potential and the corresponding times in the times array

    pylab.plot(times, potentials, label="dt=%.2f" % dt)
print("  Number of spikes: {0}".format(nest.GetStatus(sd, "n_events")[0]))

Using the pylab library the voltage trace is plotted over time

    pylab.legend(loc=3)
pylab.xlabel("time (ms)")
pylab.ylabel("V_m (mV)")

Finally the axis are labelled and a legend is generated