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Testing the adapting exponential integrate and fire model in NEST (Brette and Gerstner Fig 3D)¶
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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).
References¶
import matplotlib.pyplot as plt
import nest
import nest.voltage_trace
nest.ResetKernel()
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=20.0, 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.
nest.Simulate(1000.0)
nest.voltage_trace.from_device(voltmeter)
plt.axis([0, 1000, -85, 0])
plt.show()