# 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
# 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/>.
from .abstract_elimination import AbstractElimination
from pacman.model.decorators.overrides import overrides
[docs]class RandomByWeightElimination(AbstractElimination):
""" Elimination Rule that depends on the weight of a synapse
"""
__slots__ = [
"__prob_elim_depressed",
"__prob_elim_potentiatiated",
"__threshold"
]
def __init__(
self, threshold, prob_elim_depressed=0.0245,
prob_elim_potentiatiated=1.36 * 10 ** -4):
"""
:param 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 prob_elim_depressed:\
The probability of elimination if the weight has been depressed\
(ignored on static weight connections)
:param prob_elim_potentiatiated:\
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_potentiatiated = prob_elim_potentiatiated
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_potentiatiated * 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_potentiatiated", "threshold"]
```