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Example using Hodgkin-Huxley neuron

This example produces a rate-response (FI) curve of the Hodgkin-Huxley neuron hh_psc_alpha in response to a range of different current (DC) stimulations. The result is plotted using matplotlib.

Since a DC input affects only the neuron’s channel dynamics, this routine does not yet check correctness of synaptic response.

import nest
import numpy as np
import matplotlib.pyplot as plt


simtime = 1000

# Amplitude range, in pA
dcfrom = 0
dcstep = 20
dcto = 2000

h = 0.1  # simulation step size in mS

neuron = nest.Create('hh_psc_alpha')
sr = nest.Create('spike_recorder')

sr.record_to = 'memory'

nest.Connect(neuron, sr, syn_spec={'weight': 1.0, 'delay': h})

# Simulation loop
n_data = int(dcto / float(dcstep))
amplitudes = np.zeros(n_data)
event_freqs = np.zeros(n_data)
for i, amp in enumerate(range(dcfrom, dcto, dcstep)):
    neuron.I_e = float(amp)
    print(f"Simulating with current I={amp} pA")
    nest.Simulate(1000)  # one second warm-up time for equilibrium state
    sr.n_events = 0  # then reset spike counts
    nest.Simulate(simtime)  # another simulation call to record firing rate

    n_events = sr.n_events
    amplitudes[i] = amp
    event_freqs[i] = n_events / (simtime / 1000.)

plt.plot(amplitudes, event_freqs)

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

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