Source code for spynnaker.pyNN.models.neuron.builds.if_curr_alpha
# 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 SynapseTypeAlpha
from spynnaker.pyNN.models.neuron.input_types import InputTypeCurrent
from spynnaker.pyNN.models.neuron.threshold_types import ThresholdTypeStatic
class IFCurrAlpha(AbstractPyNNNeuronModelStandard):
""" Leaky integrate and fire neuron with an alpha-shaped current-based\
input.
: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 v: :math:`V_{init}`
:param float exc_response: :math:`response^\\mathrm{linear}_e`
:param float exc_exp_response: :math:`response^\\mathrm{exponential}_e`
:param float inh_response: :math:`response^\\mathrm{linear}_i`
:param float inh_exp_response: :math:`response^\\mathrm{exponential}_i`
"""
@default_initial_values({
"v", "exc_response", "exc_exp_response", "inh_response",
"inh_exp_response"})
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=0.5, tau_syn_I=0.5, tau_refrac=0.1,
i_offset=0.0, v=-65.0, exc_response=0.0, exc_exp_response=0.0,
inh_response=0.0, inh_exp_response=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 = SynapseTypeAlpha(
exc_response, exc_exp_response, tau_syn_E, inh_response,
inh_exp_response, tau_syn_I)
input_type = InputTypeCurrent()
threshold_type = ThresholdTypeStatic(v_thresh)
super(IFCurrAlpha, self).__init__(
model_name="IF_curr_alpha", binary="IF_curr_alpha.aplx",
neuron_model=neuron_model, input_type=input_type,
synapse_type=synapse_type, threshold_type=threshold_type)