# -*- coding: utf-8 -*-
#
# test_pp_psc_delta.py
#
# This file is part of NEST.
#
# Copyright (C) 2004 The NEST Initiative
#
# NEST is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# NEST is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with NEST. If not, see <http://www.gnu.org/licenses/>.
import unittest
import nest
import numpy as np
[docs]class PpPscDeltaTestCase(unittest.TestCase):
"""Tests for pp_psc_delta"""
[docs] def test_rate_and_fixed_dead_time(self):
"""
Check for reasonable firing rate and if fixed dead-time is respected
"""
# test parameters
d = 25.0
lam = 10.0
T = 10000.0
nest.ResetKernel()
nrn = nest.Create('pp_psc_delta')
params = {'tau_m': 10.0,
'C_m': 250.0,
'dead_time': d,
'dead_time_random': False,
'dead_time_shape': 1,
'with_reset': False,
'tau_sfa': 34.0,
'q_sfa': 0.0, # // mV, reasonable default is 7 mV
'c_1': 0.0,
'c_2': lam,
'c_3': 0.25,
'I_e': 0.0,
't_ref_remaining': 0.0
}
nest.SetStatus(nrn, params)
sd = nest.Create('spike_detector')
nest.Connect(nrn, sd)
nest.Simulate(T)
spikes = nest.GetStatus(sd)[0]['events']['times']
rate_sim = len(spikes) / (T * 1e-3)
rate_ana = 1. / (1. / lam + d * 1e-3)
ratio = rate_sim / rate_ana
# This could fail due to bad luck. However, if it passes once,
# then it should always do so, since the random numbers are
# reproducible in NEST.
self.assertLess(0.5, ratio)
self.assertLess(ratio, 1.5)
isi = np.diff(spikes)
self.assertGreaterEqual(min(isi), d)
[docs] def test_random_dead_time(self):
"""Check if random dead-time moments are respected."""
# test parameters
d = 50.0
n = 10
lam = 1.0e6
T = 10000.0
nest.ResetKernel()
nrn = nest.Create('pp_psc_delta')
params = {'tau_m': 10.0,
'C_m': 250.0,
'dead_time': d,
'dead_time_random': True,
'dead_time_shape': 10,
'with_reset': False,
'tau_sfa': 34.0,
'q_sfa': 0.0, # // mV, reasonable default is 7 mV
'c_1': 0.0,
'c_2': lam,
'c_3': 0.25,
'I_e': 0.0,
't_ref_remaining': 0.0
}
nest.SetStatus(nrn, params)
sd = nest.Create('spike_detector')
nest.Connect(nrn, sd)
nest.Simulate(T)
spikes = nest.GetStatus(sd)[0]['events']['times']
rate_sim = len(spikes) / (T * 1e-3)
rate_ana = 1. / (1. / lam + d * 1e-3)
ratio = rate_sim / rate_ana
# This could fail due to bad luck. However, if it passes once,
# then it should always do so, since the random numbers are
# reproducible in NEST.
self.assertLess(0.5, ratio)
self.assertLess(ratio, 1.5)
isi = np.diff(spikes)
# compute moments of ISI to get mean and variance
isi_mean = np.mean(isi)
isi_var = np.var(isi)
ratio_mean = isi_mean / d
self.assertLessEqual(0.5, ratio_mean)
self.assertLessEqual(ratio_mean, 1.5)
isi_var_th = n / (n / d) ** 2
ratio_var = isi_var / isi_var_th
self.assertLessEqual(0.5, ratio_var)
self.assertLessEqual(ratio_var, 1.5)
[docs] def test_adapting_threshold(self):
"""Check if threshold adaptation works by looking for negative serial
correlation of ISI."""
# test parameters
d = 1e-8
lam = 30.0
T = 10000.0
nest.ResetKernel()
nrn = nest.Create('pp_psc_delta')
params = {'tau_m': 10.0,
'C_m': 250.0,
'dead_time': d,
'dead_time_random': False,
'dead_time_shape': 1,
'with_reset': False,
'tau_sfa': 34.0,
'q_sfa': 7.0, # // mV, reasonable default is 7 mV
'c_1': 0.0,
'c_2': lam,
'c_3': 0.25,
'I_e': 0.0,
't_ref_remaining': 0.0
}
nest.SetStatus(nrn, params)
sd = nest.Create('spike_detector')
nest.Connect(nrn, sd)
nest.Simulate(T)
spikes = nest.GetStatus(sd)[0]['events']['times']
# This could fail due to bad luck. However, if it passes once,
# then it should always do so, since the random numbers are
# reproducible in NEST. Adaptive threshold changes rate, thus
# the ratio is not asserted here.
isi = np.diff(spikes)
isi_mean = np.mean(isi)
isi_var = np.var(isi)
isi_12 = np.sum(isi[:-1] * isi[1:])
isi_corr = (isi_12 / (len(isi) - 1) - isi_mean ** 2) / isi_var
self.assertLessEqual(-1.0, isi_corr)
self.assertLessEqual(isi_corr, 0.0)
[docs]def suite():
suite = unittest.makeSuite(PpPscDeltaTestCase, 'test')
return suite
[docs]def run():
runner = unittest.TextTestRunner(verbosity=2)
runner.run(suite())
if __name__ == "__main__":
run()
