sPyNNaker neural_modelling 7.3.1
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weight.h
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1/*
2 * Copyright (c) 2015 The University of Manchester
3 *
4 * Licensed under the Apache License, Version 2.0 (the "License");
5 * you may not use this file except in compliance with the License.
6 * You may obtain a copy of the License at
7 *
8 * https://www.apache.org/licenses/LICENSE-2.0
9 *
10 * Unless required by applicable law or agreed to in writing, software
11 * distributed under the License is distributed on an "AS IS" BASIS,
12 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13 * See the License for the specific language governing permissions and
14 * limitations under the License.
15 */
16
33#ifndef _WEIGHT_H_
34#define _WEIGHT_H_
35
37#include <neuron/synapse_row.h>
38
50 address_t address, uint32_t n_synapse_types,
51 uint32_t *ring_buffer_to_input_buffer_left_shifts);
52
59static weight_state_t weight_get_initial(weight_t weight, index_t synapse_type);
60
66static weight_t weight_get_final(weight_state_t new_state);
67
74static void weight_decay(weight_state_t *state, int32_t decay);
75
82
83#endif // _WEIGHT_H_
uint32_t index_t
uint32_t * address_t
#define decay(x, d)
This is a type-generic decay "function".
Definition decay.h:118
Data type definitions for SpiNNaker Neuron-modelling.
static uint32_t n_synapse_types
The number of synapse types.
Definition neuron.c:51
implementation for handling the processing of synapse rows.
address_t weight_initialise(address_t address, uint32_t n_synapse_types, uint32_t *ring_buffer_to_input_buffer_left_shifts)
Initialises the weight aspect of an STDP rule.
static weight_t weight_get_final(weight_state_t new_state)
Gets the final weight.
static weight_state_t weight_get_initial(weight_t weight, index_t synapse_type)
Gets the initial weight state.
static void weight_decay(weight_state_t *state, int32_t decay)
Decay the weight inside the state by multiplication.
static accum weight_get_update(weight_state_t state)
Get the weight inside during update in STDP fixed point format.
The current state data for the rule.