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

# 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 spinn_front_end_common.utilities.constants import BYTES_PER_WORD
from spinn_front_end_common.utilities.globals_variables import (
from spynnaker.pyNN.models.neuron.plasticity.stdp.common import (
from spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence import (
from spynnaker.pyNN.models.neuron.plasticity.stdp.synapse_structure import (

[docs]class TimingDependencePfisterSpikeTriplet(AbstractTimingDependence): """ A timing dependence STDP rule based on spike triplets. Jean-Pascal Pfister, Wulfram Gerstner. Triplets of Spikes in a Model of Spike Timing-Dependent Plasticity. *Journal of Neuroscience*, 20 September 2006, 26 (38) 9673-9682; DOI: 10.1523/JNEUROSCI.1425-06.2006 """ __slots__ = [ "__synapse_structure", "__tau_minus", "__tau_minus_data", "__tau_plus", "__tau_plus_data", "__tau_x", "__tau_x_data", "__tau_y", "__tau_y_data", "__a_plus", "__a_minus"] __PARAM_NAMES = ('tau_plus', 'tau_minus', 'tau_x', 'tau_y') # noinspection PyPep8Naming def __init__(self, tau_plus, tau_minus, tau_x, tau_y, A_plus, A_minus): r""" :param float tau_plus: :math:`\tau_+` :param float tau_minus: :math:`\tau_-` :param float tau_x: :math:`\tau_x` :param float tau_y: :math:`\tau_y` :param float A_plus: :math:`A^+` :param float A_minus: :math:`A^-` """ self.__tau_plus = tau_plus self.__tau_minus = tau_minus self.__tau_x = tau_x self.__tau_y = tau_y self.__a_plus = A_plus self.__a_minus = A_minus self.__synapse_structure = SynapseStructureWeightOnly() ts = machine_time_step_ms() self.__tau_plus_data = get_exp_lut_array(ts, self.__tau_plus) self.__tau_minus_data = get_exp_lut_array(ts, self.__tau_minus) self.__tau_x_data = get_exp_lut_array(ts, self.__tau_x, shift=2) self.__tau_y_data = get_exp_lut_array(ts, self.__tau_y, shift=2) @property def tau_plus(self): r""" :math:`\tau_+` :rtype: float """ return self.__tau_plus @property def tau_minus(self): r""" :math:`\tau_-` :rtype: float """ return self.__tau_minus @property def tau_x(self): r""" :math:`\tau_x` :rtype: float """ return self.__tau_x @property def tau_y(self): r""" :math:`\tau_y` :rtype: float """ return self.__tau_y @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 not isinstance( timing_dependence, TimingDependencePfisterSpikeTriplet): return False return ( (self.__tau_plus == timing_dependence.tau_plus) and (self.__tau_minus == timing_dependence.tau_minus) and (self.__tau_x == timing_dependence.tau_x) and (self.__tau_y == timing_dependence.tau_y))
@property def vertex_executable_suffix(self): """ The suffix to be appended to the vertex executable for this rule :rtype: str """ return "pfister_triplet" @property def pre_trace_n_bytes(self): """ The number of bytes used by the pre-trace of the rule per neuron :rtype: int """ # Triplet rule trace entries consists of two 16-bit traces - R1 and R2 return BYTES_PER_WORD
[docs] @overrides(AbstractTimingDependence.get_parameters_sdram_usage_in_bytes) def get_parameters_sdram_usage_in_bytes(self): lut_array_words = ( len(self.__tau_plus_data) + len(self.__tau_minus_data) + len(self.__tau_x_data) + len(self.__tau_y_data)) return lut_array_words * BYTES_PER_WORD
@property def n_weight_terms(self): """ The number of weight terms expected by this timing rule :rtype: int """ return 2
[docs] @overrides(AbstractTimingDependence.write_parameters) def write_parameters(self, spec, weight_scales): # Write lookup tables spec.write_array(self.__tau_plus_data) spec.write_array(self.__tau_minus_data) spec.write_array(self.__tau_x_data) spec.write_array(self.__tau_y_data)
@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