Source code for spynnaker.pyNN.models.neuron.builds.if_curr_exp_ca2_adaptive

# 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 InputTypeCurrent
from spynnaker.pyNN.models.neuron.threshold_types import ThresholdTypeStatic
from spynnaker.pyNN.models.neuron.additional_inputs import (

[docs]class IFCurrExpCa2Adaptive(AbstractPyNNNeuronModelStandard): """ Model from Liu, Y. H., & Wang, X. J. (2001). Spike-frequency\ adaptation of a generalized leaky integrate-and-fire model neuron. \ *Journal of Computational Neuroscience*, 10(1), 25-45. \ `doi:10.1023/A:1008916026143 \ <>`_ :param float tau_m: :math:`\\tau_m` :param float cm: :math:`C_m` :param float v_rest: :math:`V_{rest}` :param float v_reset: :math:`V_{reset}` :param float v_thresh: :math:`V_{thresh}` :param float tau_syn_E: :math:`\\tau^{syn}_e` :param float tau_syn_I: :math:`\\tau^{syn}_i` :param float tau_refrac: :math:`\\tau_{refrac}` :param float i_offset: :math:`I_{offset}` :param float tau_ca2: :math:`\\tau_{\\mathrm{Ca}^{+2}}` :param float i_ca2: :math:`I_{\\mathrm{Ca}^{+2}}` :param float i_alpha: :math:`\\tau_\\alpha` :param float v: :math:`V_{init}` :param float isyn_exc: :math:`I^{syn}_e` :param float isyn_inh: :math:`I^{syn}_i` """ @default_initial_values({"v", "isyn_exc", "isyn_inh", "i_ca2"}) 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, tau_ca2=50.0, i_ca2=0.0, i_alpha=0.1, 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 = InputTypeCurrent() threshold_type = ThresholdTypeStatic(v_thresh) additional_input_type = AdditionalInputCa2Adaptive( tau_ca2, i_ca2, i_alpha) super(IFCurrExpCa2Adaptive, self).__init__( model_name="IF_curr_exp_ca2_adaptive", binary="IF_curr_exp_ca2_adaptive.aplx", neuron_model=neuron_model, input_type=input_type, synapse_type=synapse_type, threshold_type=threshold_type, additional_input_type=additional_input_type)