Source code for spynnaker.pyNN.models.neuron.builds.if_cond_exp_stoc

# Copyright (c) 2017-2019 The University of Manchester
# This program 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 3 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# GNU General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <>.

from spynnaker.pyNN.models.neuron import AbstractPyNNNeuronModelStandard
from spynnaker.pyNN.models.defaults import default_initial_values
from spynnaker.pyNN.models.neuron.neuron_models import (
from spynnaker.pyNN.models.neuron.synapse_types import SynapseTypeExponential
from spynnaker.pyNN.models.neuron.input_types import InputTypeConductance
from spynnaker.pyNN.models.neuron.threshold_types import (

[docs]class IFCondExpStoc(AbstractPyNNNeuronModelStandard): """ Leaky integrate and fire neuron with a stochastic threshold. Habenschuss S, Jonke Z, Maass W. Stochastic computations in cortical \ microcircuit models. *PLoS Computational Biology.* 2013;9(11):e1003311. \ `doi:10.1371/journal.pcbi.1003311 \ <>`_ :param tau_m: :math:`\\tau_m` :param cm: :math:`C_m` :param v_rest: :math:`V_{rest}` :param v_reset: :math:`V_{reset}` :param v_thresh: :math:`V_{thresh}` :param tau_syn_E: :math:`\\tau^{syn}_e` :param tau_syn_I: :math:`\\tau^{syn}_i` :param tau_refrac: :math:`\\tau_{refrac}` :param i_offset: :math:`I_{offset}` :param e_rev_E: :math:`E^{rev}_e` :param e_rev_I: :math:`E^{rev}_i` :param du_th: :math:`du_{thresh}` :param tau_th: :math:`\\tau_{thresh}` :param v: :math:`V_{init}` :param isyn_exc: :math:`I^{syn}_e` :param isyn_inh: :math:`I^{syn}_i` """ @default_initial_values({"v", "isyn_exc", "isyn_inh"}) def __init__( self, tau_m=20.0, cm=1.0, v_rest=-65.0, v_reset=-65.0, v_thresh=-50.0, tau_syn_E=5.0, tau_syn_I=5.0, tau_refrac=0.1, i_offset=0.0, e_rev_E=0.0, e_rev_I=-70.0, du_th=0.5, tau_th=20.0, v=-65.0, isyn_exc=0.0, isyn_inh=0.0): # pylint: disable=too-many-arguments, too-many-locals neuron_model = NeuronModelLeakyIntegrateAndFire( v, v_rest, tau_m, cm, i_offset, v_reset, tau_refrac) synapse_type = SynapseTypeExponential( tau_syn_E, tau_syn_I, isyn_exc, isyn_inh) input_type = InputTypeConductance(e_rev_E, e_rev_I) threshold_type = ThresholdTypeMaassStochastic( du_th, tau_th, v_thresh) super(IFCondExpStoc, self).__init__( model_name="IF_cond_exp_stoc", binary="IF_cond_exp_stoc.aplx", neuron_model=neuron_model, input_type=input_type, synapse_type=synapse_type, threshold_type=threshold_type)