Source code for nest.tests.test_quantal_stp_synapse

# -*- coding: utf-8 -*-
#
# test_quantal_stp_synapse.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/>.

# This script compares the two variants of the Tsodyks/Markram synapse in NEST.

import nest
import numpy
import unittest


@nest.check_stack
class QuantalSTPSynapseTestCase(unittest.TestCase):
    """Compare quantal_stp_synapse with its deterministic equivalent."""

    def test_QuantalSTPSynapse(self):
        """Compare quantal_stp_synapse with its deterministic equivalent"""
        nest.ResetKernel()
        nest.set_verbosity(100)
        n_syn = 12  # number of synapses in a connection
        n_trials = 50  # number of measurement trials

        # parameter set for facilitation
        fac_params = {"U": 0.03, "u": 0.03,
                      "tau_fac": 500., "tau_rec": 200., "weight": 1.}

        # Here we assign the parameter set to the synapse models
        t1_params = fac_params       # for tsodyks2_synapse
        t2_params = t1_params.copy()  # for furhmann_synapse

        t2_params['n'] = n_syn
        t2_params['weight'] = 1. / n_syn

        nest.SetDefaults("tsodyks2_synapse", t1_params)
        nest.SetDefaults("quantal_stp_synapse", t2_params)
        nest.SetDefaults("iaf_psc_exp", {"tau_syn_ex": 3., 'tau_m': 70.})

        source = nest.Create('spike_generator')
        nest.SetStatus(
            source,
            {
                'spike_times': [
                    30., 60., 90., 120., 150., 180., 210., 240.,
                    270., 300., 330., 360., 390., 900.]
            }
        )

        parrot = nest.Create('parrot_neuron')
        neuron = nest.Create("iaf_psc_exp", 2)

        # We must send spikes via parrot because devices cannot
        # connect through plastic synapses
        # See #478.
        nest.Connect(source, parrot)
        nest.Connect(parrot, neuron[:1], syn_spec="tsodyks2_synapse")
        nest.Connect(parrot, neuron[1:], syn_spec="quantal_stp_synapse")

        voltmeter = nest.Create("voltmeter", 2)
        nest.SetStatus(voltmeter, {"withgid": False, "withtime": True})
        t_tot = 1500.

        # the following is a dry run trial so that the synapse dynamics is
        # idential in all subsequent trials.

        nest.Simulate(t_tot)

        # Now we connect the voltmeters
        nest.Connect([voltmeter[0]], [neuron[0]])
        nest.Connect([voltmeter[1]], [neuron[1]])

        for t in range(n_trials):
            t_net = nest.GetKernelStatus('time')
            nest.SetStatus(source, {'origin': t_net})
            nest.Simulate(t_tot)

        nest.Simulate(.1)  # flush the last voltmeter events from the queue

        vm = numpy.array(nest.GetStatus([voltmeter[1]], 'events')[0]['V_m'])
        vm_reference = numpy.array(nest.GetStatus(
            [voltmeter[0]], 'events')[0]['V_m'])

        assert(len(vm) % n_trials == 0)
        n_steps = int(len(vm) / n_trials)
        vm.shape = (n_trials, n_steps)
        vm_reference.shape = (n_trials, n_steps)

        vm_mean = numpy.mean(vm, axis=0)
        vm_ref_mean = numpy.mean(vm_reference, axis=0)

        error = numpy.sqrt((vm_ref_mean - vm_mean)**2)
        self.assertTrue(numpy.max(error) < 4.0e-4)


def suite():

    suite = unittest.makeSuite(QuantalSTPSynapseTestCase, 'test')
    return suite


def run():
    runner = unittest.TextTestRunner(verbosity=2)
    runner.run(suite())


if __name__ == "__main__":
    run()