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
# 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 this program. If not, see <http://www.gnu.org/licenses/>.
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`
"""
@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)