# 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 InputTypeCurrent
from spynnaker.pyNN.models.neuron.threshold_types import ThresholdTypeStatic
from spynnaker.pyNN.models.neuron.additional_inputs import (
AdditionalInputCa2Adaptive)
[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
"""
@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)