Source code for spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.timing_dependence_recurrent

# 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 spinn_front_end_common.utilities.constants import (
    BYTES_PER_WORD, BYTES_PER_SHORT)
from spinn_front_end_common.utilities.globals_variables import (
    machine_time_step_per_ms)
from .abstract_timing_dependence import AbstractTimingDependence
from spynnaker.pyNN.models.neuron.plasticity.stdp.synapse_structure import (
    SynapseStructureWeightAccumulator)
from spynnaker.pyNN.models.neuron.plasticity.stdp.common import (
    STDP_FIXED_POINT_ONE)


[docs]class TimingDependenceRecurrent(AbstractTimingDependence): """ A timing dependence STDP rule based on recurrences. """ __slots__ = [ "__accumulator_depression_plus_one", "__accumulator_potentiation_minus_one", "__dual_fsm", "__mean_post_window", "__mean_pre_window", "__synapse_structure", "__a_plus", "__a_minus"] __PARAM_NAMES = ( 'accumulator_depression', 'accumulator_potentiation', 'mean_pre_window', 'mean_post_window', 'dual_fsm') default_parameters = { 'accumulator_depression': -6, 'accumulator_potentiation': 6, 'mean_pre_window': 35.0, 'mean_post_window': 35.0, 'dual_fsm': True} def __init__( self, accumulator_depression=default_parameters[ 'accumulator_depression'], accumulator_potentiation=default_parameters[ 'accumulator_potentiation'], mean_pre_window=default_parameters['mean_pre_window'], mean_post_window=default_parameters['mean_post_window'], dual_fsm=default_parameters['dual_fsm'], A_plus=0.01, A_minus=0.01): """ :param int accumulator_depression: :param int accumulator_potentiation: :param float mean_pre_window: :param float mean_post_window: :param bool dual_fsm: :param float A_plus: :math:`A^+` :param float A_minus: :math:`A^-` """ # pylint: disable=too-many-arguments self.__accumulator_depression_plus_one = accumulator_depression + 1 self.__accumulator_potentiation_minus_one = \ accumulator_potentiation - 1 self.__mean_pre_window = mean_pre_window self.__mean_post_window = mean_post_window self.__dual_fsm = dual_fsm self.__a_plus = A_plus self.__a_minus = A_minus self.__synapse_structure = SynapseStructureWeightAccumulator() @property def A_plus(self): r""" :math:`A^+` :rtype: float """ return self.__a_plus @A_plus.setter def A_plus(self, new_value): self.__a_plus = new_value @property def A_minus(self): r""" :math:`A^-` :rtype: float """ return self.__a_minus @A_minus.setter def A_minus(self, new_value): self.__a_minus = new_value
[docs] @overrides(AbstractTimingDependence.is_same_as) def is_same_as(self, timing_dependence): if timing_dependence is None or not isinstance( timing_dependence, TimingDependenceRecurrent): return False return ((self.__accumulator_depression_plus_one == timing_dependence.accumulator_depression_plus_one) and (self.__accumulator_potentiation_minus_one == timing_dependence.accumulator_potentiation_minus_one) and (self.__mean_pre_window == timing_dependence.mean_pre_window) and (self.__mean_post_window == timing_dependence.mean_post_window))
@property def vertex_executable_suffix(self): """ The suffix to be appended to the vertex executable for this rule :rtype: str """ if self.__dual_fsm: return "recurrent_dual_fsm" return "recurrent_pre_stochastic" @property def pre_trace_n_bytes(self): """ The number of bytes used by the pre-trace of the rule per neuron :rtype: int """ # When using the separate FSMs, pre-trace contains window length, # otherwise it's in the synapse return BYTES_PER_SHORT if self.__dual_fsm else 0
[docs] @overrides(AbstractTimingDependence.get_parameters_sdram_usage_in_bytes) def get_parameters_sdram_usage_in_bytes(self): # 2 * 32-bit parameters # 2 * LUTS with STDP_FIXED_POINT_ONE * 16-bit entries return (2 * BYTES_PER_WORD) + ( 2 * STDP_FIXED_POINT_ONE * BYTES_PER_SHORT)
@property def n_weight_terms(self): """ The number of weight terms expected by this timing rule :rtype: int """ return 1
[docs] @overrides(AbstractTimingDependence.write_parameters) def write_parameters(self, spec, weight_scales): # Write parameters spec.write_value(data=self.__accumulator_depression_plus_one, data_type=DataType.INT32) spec.write_value(data=self.__accumulator_potentiation_minus_one, data_type=DataType.INT32) # Convert mean times into machine timesteps time_step_per_ms = machine_time_step_per_ms() mean_pre_timesteps = float(self.__mean_pre_window * time_step_per_ms) mean_post_timesteps = float(self.__mean_post_window * time_step_per_ms) # Write lookup tables self._write_exp_dist_lut(spec, mean_pre_timesteps) self._write_exp_dist_lut(spec, mean_post_timesteps)
@staticmethod def _write_exp_dist_lut(spec, mean): """ :param .DataSpecificationGenerator spec: :param float mean: """ indices = numpy.arange(STDP_FIXED_POINT_ONE) inv_cdf = numpy.log(1.0 - indices/float(STDP_FIXED_POINT_ONE)) * -mean spec.write_array( inv_cdf.astype(numpy.uint16), data_type=DataType.UINT16) @property def synaptic_structure(self): """ Get the synaptic structure of the plastic part of the rows :rtype: AbstractSynapseStructure """ return self.__synapse_structure
[docs] @overrides(AbstractTimingDependence.get_parameter_names) def get_parameter_names(self): return self.__PARAM_NAMES