1# -*- coding: utf-8 -*-
2#
3# hh_psc_alpha.py
4#
5# This file is part of NEST.
6#
7# Copyright (C) 2004 The NEST Initiative
8#
9# NEST is free software: you can redistribute it and/or modify
10# it under the terms of the GNU General Public License as published by
11# the Free Software Foundation, either version 2 of the License, or
12# (at your option) any later version.
13#
14# NEST is distributed in the hope that it will be useful,
15# but WITHOUT ANY WARRANTY; without even the implied warranty of
16# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
17# GNU General Public License for more details.
18#
19# You should have received a copy of the GNU General Public License
20# along with NEST.  If not, see <http://www.gnu.org/licenses/>.
21
22"""
23Example using Hodgkin-Huxley neuron
24-----------------------------------
25
26This example produces a rate-response (FI) curve of the Hodgkin-Huxley
27neuron ``hh_psc_alpha`` in response to a range of different current (DC) stimulations.
28The result is plotted using matplotlib.
29
30Since a DC input affects only the neuron's channel dynamics, this routine
31does not yet check correctness of synaptic response.
32"""
33
34import nest
35import numpy as np
36import matplotlib.pyplot as plt
37
38nest.set_verbosity('M_WARNING')
39nest.ResetKernel()
40
41simtime = 1000
42
43# Amplitude range, in pA
44dcfrom = 0
45dcstep = 20
46dcto = 2000
47
48h = 0.1  # simulation step size in mS
49
50neuron = nest.Create('hh_psc_alpha')
51sr = nest.Create('spike_recorder')
52
53sr.record_to = 'memory'
54
55nest.Connect(neuron, sr, syn_spec={'weight': 1.0, 'delay': h})
56
57# Simulation loop
58n_data = int(dcto / float(dcstep))
59amplitudes = np.zeros(n_data)
60event_freqs = np.zeros(n_data)
61for i, amp in enumerate(range(dcfrom, dcto, dcstep)):
62    neuron.I_e = float(amp)
63    print(f"Simulating with current I={amp} pA")
64    nest.Simulate(1000)  # one second warm-up time for equilibrium state
65    sr.n_events = 0  # then reset spike counts
66    nest.Simulate(simtime)  # another simulation call to record firing rate
67
68    n_events = sr.n_events
69    amplitudes[i] = amp
70    event_freqs[i] = n_events / (simtime / 1000.)
71
72plt.plot(amplitudes, event_freqs)
73plt.show()
74