Source code for spynnaker.pyNN.models.neuron.synapse_types.synapse_type_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
# 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 <>.

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

EXC_RESPONSE = "exc_response"
EXC_EXP_RESPONSE = "exc_exp_response"
TAU_SYN_E = "tau_syn_E"
INH_RESPONSE = "inh_response"
INH_EXP_RESPONSE = "inh_exp_response"
TAU_SYN_I = "tau_syn_I"
Q_EXC = "q_exc"
Q_INH = "q_inh"

    TAU_SYN_E: "ms",
    Q_EXC: "",
    TAU_SYN_I: "ms",
    Q_INH: ""

[docs]class SynapseTypeAlpha(AbstractSynapseType): __slots__ = [ "__exc_exp_response", "__exc_response", "__inh_exp_response", "__inh_response", "__tau_syn_E", "__tau_syn_I", "__q_exc", "__q_inh"] def __init__(self, exc_response, exc_exp_response, tau_syn_E, inh_response, inh_exp_response, tau_syn_I): r""" :param exc_response: :math:`response^\mathrm{linear}_e` :type exc_response: float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function :param exc_exp_response: :math:`response^\mathrm{exponential}_e` :type exc_exp_response: float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function :param tau_syn_E: :math:`\tau^{syn}_e` :type tau_syn_E: float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function :param inh_response: :math:`response^\mathrm{linear}_i` :type inh_response: float, iterable(float), ~pyNN.random.RandomDistribution or (mapping) function :param inh_exp_response: :math:`response^\mathrm{exponential}_i` :type inh_exp_response: 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 """ super().__init__([ DataType.S1615, # exc_response DataType.S1615, # exc_exp_response DataType.S1615, # 1 / tau_syn_E^2 DataType.U032, # e^(-ts / tau_syn_E) DataType.S1615, # excitatory q DataType.S1615, # inh_response DataType.S1615, # inh_exp_response DataType.S1615, # 1 / tau_syn_I^2 DataType.U032, # e^(-ts / tau_syn_I) DataType.S1615]) # inhibitory q # pylint: disable=too-many-arguments self.__exc_response = exc_response self.__exc_exp_response = exc_exp_response self.__tau_syn_E = tau_syn_E self.__inh_response = inh_response self.__inh_exp_response = inh_exp_response self.__tau_syn_I = tau_syn_I
[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[EXC_RESPONSE] = self.__exc_response state_variables[EXC_EXP_RESPONSE] = self.__exc_exp_response state_variables[Q_EXC] = 0 state_variables[INH_RESPONSE] = self.__inh_response state_variables[INH_EXP_RESPONSE] = self.__inh_exp_response state_variables[Q_INH] = 0
[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 init = lambda x: (float(ts) / 1000.0) / (x * x) # noqa decay = lambda x: numpy.exp((-float(ts) / 1000.0) / x) # noqa # Add the rest of the data return [state_variables[EXC_RESPONSE], state_variables[EXC_EXP_RESPONSE], parameters[TAU_SYN_E].apply_operation(init), parameters[TAU_SYN_E].apply_operation(decay), state_variables[Q_EXC], state_variables[INH_RESPONSE], state_variables[INH_EXP_RESPONSE], parameters[TAU_SYN_I].apply_operation(init), parameters[TAU_SYN_I].apply_operation(decay), state_variables[Q_INH]]
[docs] @overrides(AbstractSynapseType.update_values) def update_values(self, values, parameters, state_variables): # Read the data (exc_resp, exc_exp_resp, _dt_over_tau_E_sq, _exp_tau_E, q_exc, inh_resp, inh_exp_resp, _dt_over_tau_I_sq, _exp_tau_I, q_inh) = values state_variables[EXC_RESPONSE] = exc_resp state_variables[EXC_EXP_RESPONSE] = exc_exp_resp state_variables[Q_EXC] = q_exc state_variables[INH_RESPONSE] = inh_resp state_variables[INH_EXP_RESPONSE] = inh_exp_resp state_variables[Q_INH] = q_inh
[docs] @overrides(AbstractSynapseType.get_n_synapse_types) def get_n_synapse_types(self): return 2 # EX and IH
[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 exc_response(self): return self.__exc_response @exc_response.setter def exc_response(self, exc_response): self.__exc_response = exc_response @property def tau_syn_E(self): return self.__tau_syn_E @property def inh_response(self): return self.__inh_response @property def tau_syn_I(self): return self.__tau_syn_I