# Copyright (c) 2017 The University of Manchester
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
from typing import (
Any, Dict, Iterable, Optional, Tuple, TYPE_CHECKING, Sequence, Union)
import numpy
from pyNN.standardmodels.synapses import StaticSynapse
from spinn_utilities.overrides import overrides
from pacman.model.graphs.application import ApplicationVertex
from pacman.model.graphs.common import Slice
from spynnaker.pyNN.exceptions import SynapticConfigurationException
from spynnaker.pyNN.types import Weights
from spynnaker.pyNN.utilities.utility_calls import create_mars_kiss_seeds
from .abstract_synapse_dynamics_structural import (
AbstractSynapseDynamicsStructural)
from .synapse_dynamics_structural_common import (
DEFAULT_F_REW, DEFAULT_INITIAL_WEIGHT, DEFAULT_INITIAL_DELAY,
DEFAULT_S_MAX, SynapseDynamicsStructuralCommon as
_Common)
from .abstract_static_synapse_dynamics import AbstractStaticSynapseDynamics
from .synapse_dynamics_static import SynapseDynamicsStatic
from .synapse_dynamics_stdp import SynapseDynamicsSTDP
from .synapse_dynamics_structural_stdp import SynapseDynamicsStructuralSTDP
from .abstract_synapse_dynamics import AbstractSynapseDynamics
from .synapse_dynamics_weight_changable import SynapseDynamicsWeightChangable
from .synapse_dynamics_weight_changer import SynapseDynamicsWeightChanger
if TYPE_CHECKING:
from pacman.model.graphs import AbstractVertex
from pacman.model.graphs.machine import MachineVertex
from spynnaker.pyNN.models.neural_projections import (
ProjectionApplicationEdge, SynapseInformation)
from spynnaker.pyNN.models.neural_projections.connectors import (
AbstractConnector)
from spynnaker.pyNN.models.neuron.synapse_dynamics.\
abstract_synapse_dynamics_structural import (
InitialDelay)
from spynnaker.pyNN.models.neuron.structural_plasticity.synaptogenesis.\
partner_selection.abstract_partner_selection import (
AbstractPartnerSelection)
from spynnaker.pyNN.models.neuron.structural_plasticity.synaptogenesis.\
formation.abstract_formation import (
AbstractFormation)
from spynnaker.pyNN.models.neuron.structural_plasticity.synaptogenesis.\
elimination.abstract_elimination import (
AbstractElimination)
from spynnaker.pyNN.models.neuron.synapse_dynamics.types import (
ConnectionsArray)
from spynnaker.pyNN.types import Delays
from .synapse_dynamics_structural_common import ConnectionsInfo
class SynapseDynamicsStructuralStatic(SynapseDynamicsStatic, _Common):
"""
Class that enables synaptic rewiring in the absence of STDP.
It acts as a wrapper around SynapseDynamicsStatic, meaning that rewiring
can operate in parallel with static synapses.
Written by Petrut Bogdan.
"""
__slots__ = (
# Frequency of rewiring (Hz)
"__f_rew",
# Initial weight assigned to a newly formed connection
"__initial_weight",
# Delay assigned to a newly formed connection
"__initial_delay",
# Maximum fan-in per target layer neuron
"__s_max",
# The seed
"__seed",
# Holds initial connectivity as defined via connector
"__connections",
# The actual type of weights: static through the simulation or those
# that can be change through STDP
"__weight_dynamics",
# Shared RNG seed to be written on all cores
"__seeds",
# The RNG used with the seed that is passed in
"__rng",
# The partner selection rule
"__partner_selection",
# The formation rule
"__formation",
# The elimination rule
"__elimination",
"__with_replacement")
def __init__(
self, partner_selection: AbstractPartnerSelection,
formation: AbstractFormation, elimination: AbstractElimination,
f_rew: float = DEFAULT_F_REW,
initial_weight: float = DEFAULT_INITIAL_WEIGHT,
initial_delay: InitialDelay = DEFAULT_INITIAL_DELAY,
s_max: int = DEFAULT_S_MAX,
with_replacement: bool = True, seed: Optional[int] = None,
weight: float = StaticSynapse.default_parameters['weight'],
delay: Optional[float] = None):
"""
:param partner_selection: The partner selection rule
:param formation: The formation rule
:param elimination: The elimination rule
:param f_rew: How many rewiring attempts will be done per second.
:param initial_weight:
Weight assigned to a newly formed connection
:param initial_delay:
Delay assigned to a newly formed connection; a single value means
a fixed delay value, or a tuple of two values means the delay will
be chosen at random from a uniform distribution between the given
values
:param s_max: Maximum fan-in per target layer neuron
:param with_replacement:
If set to True (default), a new synapse can be formed in a
location where a connection already exists; if False, then it must
form where no connection already exists
:param seed: seed the random number generators
:param weight: The weight of connections formed by the connector
:param delay: The delay of connections formed by the connector
Use ``None`` to get the simulator default minimum delay.
"""
super().__init__(weight=weight, delay=delay, pad_to_length=s_max)
self.__partner_selection = partner_selection
self.__formation = formation
self.__elimination = elimination
self.__f_rew = f_rew
self.__initial_weight = initial_weight
self.__initial_delay = initial_delay
self.__s_max = s_max
self.__with_replacement = with_replacement
self.__seed = seed
self.__connections: ConnectionsInfo = dict()
self.__rng = numpy.random.RandomState(seed)
self.__seeds: Dict[Any, Tuple[int, ...]] = dict()
[docs]
@overrides(AbstractStaticSynapseDynamics.merge)
def merge(self, synapse_dynamics: AbstractSynapseDynamics
) -> AbstractSynapseDynamics:
if isinstance(synapse_dynamics, (SynapseDynamicsWeightChangable,
SynapseDynamicsWeightChanger)):
raise SynapticConfigurationException(
"Weight Changer and Structural Plasticity are not currently"
" compatible")
# If the dynamics is structural, check if same as this
if isinstance(synapse_dynamics, AbstractSynapseDynamicsStructural):
if not self.is_same_as(synapse_dynamics):
raise SynapticConfigurationException(
"Synapse dynamics must match exactly when using multiple"
" edges to the same population")
# If structural part matches, return other as it might also be STDP
return synapse_dynamics
# If the dynamics is STDP but not Structural (as here), merge
if isinstance(synapse_dynamics, SynapseDynamicsSTDP):
return SynapseDynamicsStructuralSTDP(
self.partner_selection, self.formation,
self.elimination,
synapse_dynamics.timing_dependence,
synapse_dynamics.weight_dependence,
# voltage dependence is not supported
None, synapse_dynamics.dendritic_delay_fraction,
self.f_rew, self.initial_weight, self.initial_delay,
self.s_max, self.with_replacement, self.seed,
backprop_delay=synapse_dynamics.backprop_delay)
# Otherwise, it is static, so return ourselves
return self
[docs]
@overrides(AbstractStaticSynapseDynamics.is_same_as)
@overrides(_Common.is_same_as)
def is_same_as(self, synapse_dynamics: Union[
AbstractSynapseDynamics,
AbstractSynapseDynamicsStructural]) -> bool:
if not (isinstance(synapse_dynamics, SynapseDynamicsStructuralStatic)):
return False
if not AbstractStaticSynapseDynamics.is_same_as(
self, synapse_dynamics):
return False
return _Common.is_same_as(self, synapse_dynamics)
[docs]
@overrides(AbstractStaticSynapseDynamics.get_vertex_executable_suffix)
def get_vertex_executable_suffix(self) -> str:
return (super().get_vertex_executable_suffix() +
_Common.get_vertex_executable_suffix(self))
[docs]
@overrides(AbstractSynapseDynamicsStructural.set_connections)
def set_connections(
self, connections: ConnectionsArray, post_vertex_slice: Slice,
app_edge: ProjectionApplicationEdge,
synapse_info: SynapseInformation) -> None:
if not isinstance(synapse_info.synapse_dynamics,
AbstractSynapseDynamicsStructural):
return
collector = self.__connections.setdefault(
(app_edge.post_vertex, post_vertex_slice.lo_atom), [])
collector.append((connections, app_edge, synapse_info))
[docs]
@overrides(AbstractStaticSynapseDynamics.get_parameter_names)
def get_parameter_names(self) -> Iterable[str]:
yield from super().get_parameter_names()
yield from _Common.get_parameter_names(self)
@property
@overrides(SynapseDynamicsStatic.changes_during_run)
def changes_during_run(self) -> bool:
return True
@property
@overrides(AbstractSynapseDynamicsStructural.f_rew)
def f_rew(self) -> float:
return self.__f_rew
@property
@overrides(AbstractSynapseDynamicsStructural.seed)
def seed(self) -> Optional[int]:
return self.__seed
@property
@overrides(AbstractSynapseDynamicsStructural.s_max)
def s_max(self) -> int:
return self.__s_max
@property
@overrides(AbstractSynapseDynamicsStructural.with_replacement)
def with_replacement(self) -> bool:
return self.__with_replacement
@property
@overrides(AbstractSynapseDynamicsStructural.initial_weight)
def initial_weight(self) -> float:
return self.__initial_weight
@property
@overrides(AbstractSynapseDynamicsStructural.initial_delay)
def initial_delay(self) -> InitialDelay:
return self.__initial_delay
@property
@overrides(AbstractSynapseDynamicsStructural.partner_selection)
def partner_selection(self) -> AbstractPartnerSelection:
return self.__partner_selection
@property
@overrides(AbstractSynapseDynamicsStructural.formation)
def formation(self) -> AbstractFormation:
return self.__formation
@property
@overrides(AbstractSynapseDynamicsStructural.elimination)
def elimination(self) -> AbstractElimination:
return self.__elimination
@property
@overrides(_Common.connections)
def connections(self) -> ConnectionsInfo:
return self.__connections
[docs]
@overrides(SynapseDynamicsStatic.get_weight_mean)
def get_weight_mean(self, connector: AbstractConnector,
synapse_info: SynapseInformation) -> float:
return self.get_weight_maximum(connector, synapse_info)
[docs]
@overrides(SynapseDynamicsStatic.get_weight_variance)
def get_weight_variance(
self, connector: AbstractConnector, weights: Weights,
synapse_info: SynapseInformation) -> float:
return 0.0
[docs]
@overrides(SynapseDynamicsStatic.get_weight_maximum)
def get_weight_maximum(self, connector: AbstractConnector,
synapse_info: SynapseInformation) -> float:
w_m = super().get_weight_maximum(connector, synapse_info)
return max(w_m, self.__initial_weight)
[docs]
@overrides(SynapseDynamicsStatic.get_delay_maximum)
def get_delay_maximum(self, connector: AbstractConnector,
synapse_info: SynapseInformation) -> float:
d_m = super().get_delay_maximum(connector, synapse_info)
if d_m is None:
return self.__initial_delay
return max(d_m, self.__initial_delay)
[docs]
@overrides(SynapseDynamicsStatic.get_delay_minimum)
def get_delay_minimum(
self, connector: AbstractConnector,
synapse_info: SynapseInformation) -> Optional[float]:
d_m = super().get_delay_minimum(connector, synapse_info)
if d_m is None:
return self.__initial_delay
return min(d_m, self.__initial_delay)
[docs]
@overrides(SynapseDynamicsStatic.get_delay_variance)
def get_delay_variance(
self, connector: AbstractConnector, delays: Delays,
synapse_info: SynapseInformation) -> float:
return 0.0
@overrides(_Common._get_seeds)
def _get_seeds(
self, app_vertex: Union[None, ApplicationVertex, Slice] = None
) -> Sequence[int]:
if app_vertex:
if app_vertex not in self.__seeds:
self.__seeds[app_vertex] = (
create_mars_kiss_seeds(self.__rng))
return self.__seeds[app_vertex]
else:
return create_mars_kiss_seeds(self.__rng)
[docs]
@overrides(SynapseDynamicsStatic.generate_on_machine)
def generate_on_machine(self) -> bool:
# Never generate structural connections on the machine
return False
[docs]
@overrides(AbstractSynapseDynamics.get_connected_vertices)
def get_connected_vertices(
self, s_info: SynapseInformation, source_vertex: ApplicationVertex,
target_vertex: ApplicationVertex) -> Sequence[
Tuple[MachineVertex, Sequence[AbstractVertex]]]:
# Things change, so assume all connected
return [(m_vertex, [source_vertex])
for m_vertex in target_vertex.splitter.get_in_coming_vertices(
s_info.partition_id)]
@property
@overrides(AbstractSynapseDynamics.is_combined_core_capable)
def is_combined_core_capable(self) -> bool:
return False
@property
@overrides(AbstractSynapseDynamics.is_split_core_capable)
def is_split_core_capable(self) -> bool:
return True
@property
@overrides(AbstractStaticSynapseDynamics.synapses_per_second)
def synapses_per_second(self) -> int:
return (super().synapses_per_second -
super().reduction_synapses_per_second)