Source code for spynnaker.pyNN.models.neuron.threshold_types.threshold_type_maass_stochastic
# Copyright (c) 2017 The University of Manchester
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from spinn_utilities.overrides import overrides
from spinn_utilities.ranged import RangeDictionary
from spinn_front_end_common.interface.ds import DataType
from spynnaker.pyNN.models.neuron.implementations import ModelParameter
from spynnaker.pyNN.utilities.struct import Struct
from spynnaker.pyNN.data import SpynnakerDataView
from .abstract_threshold_type import AbstractThresholdType
DU_TH = "du_th"
TAU_TH = "tau_th"
V_THRESH = "v_thresh"
TIMESTEP = "timestep"
class ThresholdTypeMaassStochastic(AbstractThresholdType):
"""
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>`_
"""
__slots__ = (
"__du_th",
"__tau_th",
"__v_thresh")
def __init__(self, du_th: ModelParameter, tau_th: ModelParameter,
v_thresh: ModelParameter):
r"""
:param du_th: :math:`du_{thresh}`
:type du_th: float or iterable(float) or
~spynnaker.pyNN.RandomDistribution or (mapping) function
:param tau_th: :math:`\tau_{thresh}`
:type tau_th: float or iterable(float) or
~spynnaker.pyNN.RandomDistribution or (mapping) function
:param v_thresh: :math:`V_{thresh}`
:type v_thresh: float or iterable(float) or
~spynnaker.pyNN.RandomDistribution or (mapping) function
"""
super().__init__(
[Struct([
(DataType.S1615, DU_TH),
(DataType.S1615, TAU_TH),
(DataType.S1615, V_THRESH),
(DataType.S1615, TIMESTEP)])],
{DU_TH: "mV", TAU_TH: "ms", V_THRESH: "mV"})
self.__du_th = du_th
self.__tau_th = tau_th
self.__v_thresh = v_thresh
[docs]
@overrides(AbstractThresholdType.add_parameters)
def add_parameters(self, parameters: RangeDictionary[float]):
parameters[DU_TH] = self._convert(self.__du_th)
parameters[TAU_TH] = self._convert(self.__tau_th)
parameters[V_THRESH] = self._convert(self.__v_thresh)
parameters[TIMESTEP] = SpynnakerDataView.get_simulation_time_step_ms()
[docs]
@overrides(AbstractThresholdType.add_state_variables)
def add_state_variables(self, state_variables: RangeDictionary[float]):
pass
@property
def v_thresh(self) -> ModelParameter:
"""
:math:`V_{thresh}`
"""
return self.__v_thresh
@property
def du_th(self) -> ModelParameter:
"""
:math:`du_{thresh}`
"""
return self.__du_th
@property
def tau_th(self) -> ModelParameter:
r"""
:math:`\tau_{thresh}`
"""
return self.__tau_th