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

# Copyright (c) 2017-2019 The University of Manchester
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from spynnaker.pyNN.models.defaults import default_initial_values
from spynnaker.pyNN.models.neuron.neuron_models import (
    NeuronModelLeakyIntegrateAndFire)
from spynnaker.pyNN.models.neuron import AbstractPyNNNeuronModelStandard
from spynnaker.pyNN.models.neuron.synapse_types import SynapseTypeSEMD
from spynnaker.pyNN.models.neuron.input_types import InputTypeCurrent
from spynnaker.pyNN.models.neuron.threshold_types import ThresholdTypeStatic


[docs]class IFCurrExpSEMDBase(AbstractPyNNNeuronModelStandard): """ Leaky integrate and fire neuron with an exponentially decaying\ current input, where the excitatory input depends upon the inhibitory\ input (see https://www.cit-ec.de/en/nbs/spiking-insect-vision) :param tau_m: :math:`\\tau_m` :type tau_m: float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function :param cm: :math:`C_m` :type cm: float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function :param v_rest: :math:`V_{rest}` :type v_rest: float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function :param v_reset: :math:`V_{reset}` :type v_reset: float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function :param v_thresh: :math:`V_{thresh}` :type v_thresh: float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function :param tau_syn_E: :math:`\\tau^{syn}_{e_1}` :type tau_syn_E: float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function :param tau_syn_E2: :math:`\\tau^{syn}_{e_2}` :type tau_syn_E2: float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function :param tau_syn_I: :math:`\\tau^{syn}_i` :type tau_syn_I: float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function :param tau_refrac: :math:`\\tau_{refrac}` :type tau_refrac: float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function :param i_offset: :math:`I_{offset}` :type i_offset: float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function :param v: :math:`V_{init}` :type v: float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function :param isyn_exc: :math:`I^{syn}_{e_1}` :type isyn_exc: float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function :param isyn_exc2: :math:`I^{syn}_{e_2}` :type isyn_exc2: float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function :param isyn_inh: :math:`I^{syn}_i` :type isyn_inh: float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function :param multiplicator: :type multiplicator: float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function :param exc2_old: :type exc2_old: float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function :param scaling_factor: :type scaling_factor: float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function """ @default_initial_values({"v", "isyn_exc", "isyn_exc2", "isyn_inh", "exc2_old"}) 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_E2=5.0, tau_syn_I=5.0, tau_refrac=0.1, i_offset=0.0, v=-65.0, isyn_exc=0.0, isyn_exc2=0.0, isyn_inh=0.0, multiplicator=0.0, exc2_old=0.0, scaling_factor=1.0): neuron_model = NeuronModelLeakyIntegrateAndFire( v, v_rest, tau_m, cm, i_offset, v_reset, tau_refrac) synapse_type = SynapseTypeSEMD( tau_syn_E, tau_syn_E2, tau_syn_I, isyn_exc, isyn_exc2, isyn_inh, multiplicator, exc2_old, scaling_factor) input_type = InputTypeCurrent() threshold_type = ThresholdTypeStatic(v_thresh) super().__init__( model_name="IF_curr_exp_SEMD", binary="IF_curr_exp_sEMD.aplx", neuron_model=neuron_model, input_type=input_type, synapse_type=synapse_type, threshold_type=threshold_type)