Testing the adapting exponential integrate and fire model in NEST (Brette and Gerstner Fig 3D)

This example tests the adaptive integrate and fire model (AdEx) according to Brette and Gerstner [1] reproduces Figure 3D of the paper.

Note that Brette and Gerstner give the value for b in nA. To be consistent with the other parameters in the equations, b must be converted to pA (pico Ampere).


import nest
import nest.voltage_trace
import matplotlib.pyplot as plt


First we make sure that the resolution of the simulation is 0.1 ms. This is important, since the slop of the action potential is very steep.

nest.resolution = 0.1
neuron = nest.Create("aeif_cond_exp")

Set the parameters of the neuron according to the paper.

neuron.set(V_peak=20., E_L=-60.0, a=80.0, b=80.5, tau_w=720.0)

Create and configure the stimulus which is a step current.

dc = nest.Create("dc_generator")

dc.set(amplitude=-800.0, start=0.0, stop=400.0)

We connect the DC generators.

nest.Connect(dc, neuron, 'all_to_all')

And add a voltmeter to sample the membrane potentials from the neuron in intervals of 0.1 ms.

voltmeter = nest.Create("voltmeter", params={'interval': 0.1})
nest.Connect(voltmeter, neuron)

Finally, we simulate for 1000 ms and plot a voltage trace to produce the figure.


plt.axis([0, 1000, -85, 0])

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

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