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

# Copyright (c) 2017-2019 The University of Manchester
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# 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
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# This program is distributed in the hope that it will be useful,
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from spynnaker.pyNN.models.neuron import AbstractPyNNNeuronModelStandard
from spynnaker.pyNN.models.defaults import default_initial_values
from spynnaker.pyNN.models.neuron.neuron_models import (
    NeuronModelLeakyIntegrateAndFire)
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 (
    ThresholdTypeMaassStochastic)


[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 <https://doi.org/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` :type tau_m: float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function :type cm: float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function :type v_rest: float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function :type v_reset: float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function :type v_thresh: float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function :type tau_syn_E: float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function :type tau_syn_I: float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function :type tau_refrac: float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function :type i_offset: float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function :type e_rev_E: float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function :type e_rev_I: float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function :type du_th: float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function :type tau_th: float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function :type v: Float, float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function :type isyn_exc: float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function :type isyn_inh: float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function """ @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().__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)