Source code for spynnaker.pyNN.models.neuron.structural_plasticity.synaptogenesis.elimination.random_by_weight_elimination

# 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 <>.

from spinn_utilities.overrides import overrides
from .abstract_elimination import AbstractElimination

[docs]class RandomByWeightElimination(AbstractElimination): """ Elimination Rule that depends on the weight of a synapse """ __slots__ = [ "__prob_elim_depressed", "__prob_elim_potentiated", "__threshold" ] def __init__( self, threshold, prob_elim_depressed=0.0245, prob_elim_potentiated=1.36 * 10 ** -4): """ :param float threshold: Below this weight is considered depression, above or equal to this weight is considered potentiation (or the static weight of the connection on static weight connections) :param float prob_elim_depressed: The probability of elimination if the weight has been depressed (ignored on static weight connections) :param float prob_elim_potentiated: The probability of elimination of the weight has been potentiated or has not changed (and also used on static weight connections) """ self.__prob_elim_depressed = prob_elim_depressed self.__prob_elim_potentiated = prob_elim_potentiated self.__threshold = threshold @property @overrides(AbstractElimination.vertex_executable_suffix) def vertex_executable_suffix(self): return "_weight"
[docs] @overrides(AbstractElimination.get_parameters_sdram_usage_in_bytes) def get_parameters_sdram_usage_in_bytes(self): return 3 * 4
[docs] @overrides(AbstractElimination.write_parameters) def write_parameters(self, spec, weight_scale): spec.write_value(int(self.__prob_elim_depressed * 0xFFFFFFFF)) spec.write_value(int(self.__prob_elim_potentiated * 0xFFFFFFFF)) spec.write_value(self.__threshold * weight_scale)
[docs] @overrides(AbstractElimination.get_parameter_names) def get_parameter_names(self): return ["prob_elim_depressed", "prob_elim_potentiated", "threshold"]