Source code for spynnaker.pyNN.models.neuron.synapse_types.synapse_type_exponential

# 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/>.

import numpy
from spinn_utilities.overrides import overrides
from data_specification.enums import DataType
from .abstract_synapse_type import AbstractSynapseType

TAU_SYN_E = 'tau_syn_E'
TAU_SYN_I = 'tau_syn_I'
ISYN_EXC = "isyn_exc"
ISYN_INH = "isyn_inh"

UNITS = {
    TAU_SYN_E: "mV",
    TAU_SYN_I: 'mV',
    ISYN_EXC: "",
    ISYN_INH: "",
}


[docs]class SynapseTypeExponential(AbstractSynapseType): __slots__ = [ "__tau_syn_E", "__tau_syn_I", "__isyn_exc", "__isyn_inh"] def __init__(self, tau_syn_E, tau_syn_I, isyn_exc, isyn_inh): r""" :param tau_syn_E: :math:`\tau^{syn}_e` :type tau_syn_E: float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function :param tau_syn_I: :math:`\tau^{syn}_i` :type tau_syn_I: float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function :param isyn_exc: :math:`I^{syn}_e` :type isyn_exc: float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function :param isyn_inh: :math:`I^{syn}_i` :type isyn_inh: float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function """ super().__init__([ DataType.U032, # decay_E DataType.U032, # init_E DataType.S1615, # isyn_exc DataType.U032, # decay_I DataType.U032, # init_I DataType.S1615]) # isyn_inh self.__tau_syn_E = tau_syn_E self.__tau_syn_I = tau_syn_I self.__isyn_exc = isyn_exc self.__isyn_inh = isyn_inh
[docs] @overrides(AbstractSynapseType.get_n_cpu_cycles) def get_n_cpu_cycles(self, n_neurons): return 100 * n_neurons
[docs] @overrides(AbstractSynapseType.add_parameters) def add_parameters(self, parameters): parameters[TAU_SYN_E] = self.__tau_syn_E parameters[TAU_SYN_I] = self.__tau_syn_I
[docs] @overrides(AbstractSynapseType.add_state_variables) def add_state_variables(self, state_variables): state_variables[ISYN_EXC] = self.__isyn_exc state_variables[ISYN_INH] = self.__isyn_inh
[docs] @overrides(AbstractSynapseType.get_units) def get_units(self, variable): return UNITS[variable]
[docs] @overrides(AbstractSynapseType.has_variable) def has_variable(self, variable): return variable in UNITS
[docs] @overrides(AbstractSynapseType.get_values) def get_values(self, parameters, state_variables, vertex_slice, ts): """ :param int ts: machine time step """ # pylint: disable=arguments-differ tsfloat = float(ts) / 1000.0 decay = lambda x: numpy.exp(-tsfloat / x) # noqa E731 init = lambda x: (x / tsfloat) * (1.0 - numpy.exp(-tsfloat / x)) # noqa E731 # Add the rest of the data return [parameters[TAU_SYN_E].apply_operation(decay), parameters[TAU_SYN_E].apply_operation(init), state_variables[ISYN_EXC], parameters[TAU_SYN_I].apply_operation(decay), parameters[TAU_SYN_I].apply_operation(init), state_variables[ISYN_INH]]
[docs] @overrides(AbstractSynapseType.update_values) def update_values(self, values, parameters, state_variables): # Read the data (_decay_E, _init_E, isyn_exc, _decay_I, _init_I, isyn_inh) = values state_variables[ISYN_EXC] = isyn_exc state_variables[ISYN_INH] = isyn_inh
[docs] @overrides(AbstractSynapseType.get_n_synapse_types) def get_n_synapse_types(self): return 2
[docs] @overrides(AbstractSynapseType.get_synapse_id_by_target) def get_synapse_id_by_target(self, target): if target == "excitatory": return 0 elif target == "inhibitory": return 1 return None
[docs] @overrides(AbstractSynapseType.get_synapse_targets) def get_synapse_targets(self): return "excitatory", "inhibitory"
@property def tau_syn_E(self): return self.__tau_syn_E @property def tau_syn_I(self): return self.__tau_syn_I @property def isyn_exc(self): return self.__isyn_exc @property def isyn_inh(self): return self.__isyn_inh