sPyNNaker neural_modelling 7.3.1
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neuron_impl_stoc_exp.h
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1/*
2 * Copyright (c) 2023 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
19
20#ifndef _NEURON_IMPL_STOC_EXP_
21#define _NEURON_IMPL_STOC_EXP_
22
24#include <spin1_api.h>
25#include <debug.h>
26#include <random.h>
27#include <stdfix-full-iso.h>
28#include <common/maths-util.h>
29
30#define V_RECORDING_INDEX 0
31#define EX_INPUT_INDEX 1
32#define IN_INPUT_INDEX 2
33#define PROB_INDEX 3
34#define N_RECORDED_VARS 4
35
36#define SPIKE_RECORDING_BITFIELD 0
37#define N_BITFIELD_VARS 1
38
40
43
44#include "stoc_exp_common.h"
45
64
65
87
90
91static bool neuron_impl_initialise(uint32_t n_neurons) {
92 // Allocate DTCM for neuron array
93 neuron_array = spin1_malloc(n_neurons * sizeof(neuron_impl_t));
94 if (neuron_array == NULL) {
95 log_error("Unable to allocate neuron array - Out of DTCM");
96 return false;
97 }
98
99 return true;
100}
101
102static inline void neuron_model_initialise(
104 UREAL ts = params->time_step;
105 state->tau_recip = ulrbits((
106 bitsuk(ukdivuk(UREAL_CONST(1.0), params->tau_ms)) & 0xFFFF) << 16);
107 state->bias = params->bias;
108 state->t_refract = stoc_exp_ceil_accum(ukdivuk(params->tau_ms, ts));
109 state->refract_timer = params->refract_init;
110 spin1_memcpy(state->random_seed, params->random_seed, sizeof(mars_kiss64_seed_t));
112
113 // Reset the inputs
114 state->inputs[0] = ZERO;
115 state->inputs[1] = ZERO;
116}
117
118static inline void neuron_model_save_state(neuron_impl_t *state, neuron_params_t *params) {
119 params->refract_init = state->refract_timer;
120 spin1_memcpy(params->random_seed, state->random_seed, sizeof(mars_kiss64_seed_t));
121}
122
123static void neuron_impl_load_neuron_parameters(
124 address_t address, uint32_t next, uint32_t n_neurons,
125 address_t save_initial_state) {
126
127 neuron_params_t *params = (neuron_params_t *) &address[next];
128 for (uint32_t i = 0; i < n_neurons; i++) {
129 neuron_model_initialise(&neuron_array[i], &params[i]);
130 }
131
132 // If we are to save the initial state, copy the whole of the parameters
133 // to the initial state
134 if (save_initial_state) {
135 spin1_memcpy(save_initial_state, address,
136 n_neurons * sizeof(neuron_params_t));
137 }
138}
139
140static void neuron_impl_store_neuron_parameters(
141 address_t address, uint32_t next, uint32_t n_neurons) {
142 neuron_params_t *params = (neuron_params_t *) &address[next];
143 for (uint32_t i = 0; i < n_neurons; i++) {
144 neuron_model_save_state(&neuron_array[i], &params[i]);
145 }
146}
147
148static void neuron_impl_add_inputs(
149 index_t synapse_type_index, index_t neuron_index,
150 input_t weights_this_timestep) {
151 // Get the neuron itself
152 neuron_impl_t *neuron = &neuron_array[neuron_index];
153
154 // Do something to store the inputs for the next state update
155 neuron->inputs[synapse_type_index] += weights_this_timestep;
156}
157
158// Update done when in refractory
159static inline void do_refrac_update(uint32_t timer_count, uint32_t time,
160 uint32_t neuron_index, neuron_impl_t *neuron) {
161 neuron->refract_timer -= 1;
162
163 // Record things
164 neuron_recording_record_int32(PROB_INDEX, neuron_index, 0);
165 neuron_recording_record_accum(V_RECORDING_INDEX, neuron_index, ZERO);
166 neuron_recording_record_accum(EX_INPUT_INDEX, neuron_index, neuron->inputs[0]);
167 neuron_recording_record_accum(IN_INPUT_INDEX, neuron_index, neuron->inputs[1]);
168
169 // Reset the inputs
170 neuron->inputs[0] = ZERO;
171 neuron->inputs[1] = ZERO;
172
173 // Send a spike
174 neuron_recording_record_bit(SPIKE_RECORDING_BITFIELD, neuron_index);
175 send_spike(timer_count, time, neuron_index);
176}
177
178static inline void do_non_refrac_update(uint32_t timer_count, uint32_t time,
179 uint32_t neuron_index, neuron_impl_t *neuron) {
180 // Work out the membrane voltage
181 REAL v_membrane = (neuron->bias + neuron->inputs[0]) - neuron->inputs[1];
182
183 // Record things
184 neuron_recording_record_accum(V_RECORDING_INDEX, neuron_index, v_membrane);
185 neuron_recording_record_accum(EX_INPUT_INDEX, neuron_index, neuron->inputs[0]);
186 neuron_recording_record_accum(IN_INPUT_INDEX, neuron_index, neuron->inputs[1]);
187
188 // Reset the inputs
189 neuron->inputs[0] = ZERO;
190 neuron->inputs[1] = ZERO;
191
192 // Work out the probability
193 uint32_t prob = get_probability(neuron->tau_recip, v_membrane);
194
195 // Record the probability
196 neuron_recording_record_int32(PROB_INDEX, neuron_index, (int32_t) prob);
197
198 // Get a random number
199 uint32_t random = mars_kiss64_seed(neuron->random_seed);
200
201 // If the random number is less than the probability value, spike
202 if (random < prob) {
203 neuron->refract_timer = neuron->t_refract - 1;
204 neuron_recording_record_bit(SPIKE_RECORDING_BITFIELD, neuron_index);
205 send_spike(timer_count, time, neuron_index);
206 }
207}
208
209static void neuron_impl_do_timestep_update(
210 uint32_t timer_count, uint32_t time, uint32_t n_neurons) {
211 for (uint32_t neuron_index = 0; neuron_index < n_neurons; neuron_index++) {
212 // Get the neuron itself
213 neuron_impl_t *neuron = &neuron_array[neuron_index];
214
215 // If in refractory, count down and spike!
216 if (neuron->refract_timer > 0) {
217 do_refrac_update(timer_count, time, neuron_index, neuron);
218 } else {
219 do_non_refrac_update(timer_count, time, neuron_index, neuron);
220 }
221 }
222}
223
224#if LOG_LEVEL >= LOG_DEBUG
225static void neuron_impl_print_inputs(uint32_t n_neurons) {
226 log_debug("-------------------------------------\n");
227 for (index_t i = 0; i < n_neurons; i++) {
228 neuron_impl_t *neuron = &neuron_array[i];
229 log_debug("inputs: %k %k", neuron->inputs[0], neuron->inputs[1]);
230 }
231 log_debug("-------------------------------------\n");
232}
233
235 // there aren't any accessible
236 use(n_neurons);
237}
238
239static const char *neuron_impl_get_synapse_type_char(uint32_t synapse_type) {
240 if (synapse_type == 0) {
241 return 'E';
242 } else if (synapse_type == 1) {
243 return 'I';
244 }
245 return 'U';
246}
247#endif // LOG_LEVEL >= LOG_DEBUG
248
249
250#endif // _NEURON_IMPL_STOC_EXP_
uint32_t index_t
#define use(x)
uint32_t * address_t
General API of a current source implementation.
Implement all current sources.
void log_error(const char *message,...)
void log_debug(const char *message,...)
maths-util.h - first created 7/10/2013 version 0.1
unsigned accum UREAL
Type used for "unsigned real" numbers.
Definition maths-util.h:94
accum REAL
Type used for "real" numbers.
Definition maths-util.h:91
unsigned long fract UFRACT
Type used for "unsigned fractional" numbers.
Definition maths-util.h:100
#define UREAL_CONST(x)
Define a constant of type UREAL.
Definition maths-util.h:108
static UREAL ukdivuk(UREAL a, UREAL b)
Divides an unsigned accum by another unsigned accum.
Definition maths-util.h:242
#define ZERO
A REAL 0.0.
Definition maths-util.h:123
REAL input_t
The type of an input.
static uint32_t n_neurons
The number of neurons on the core.
Definition neuron.c:45
General API of a neuron implementation.
void neuron_impl_print_synapse_parameters(uint32_t n_neurons)
Print the synapse parameters of the neurons.
const char * neuron_impl_get_synapse_type_char(uint32_t synapse_type)
Get the synapse type character for a synapse type.
void neuron_impl_print_inputs(uint32_t n_neurons)
Print the inputs to the neurons.
static neuron_impl_t * neuron_array
Array of neuron states.
UFRACT tau_recip
The reciprocal of the tau value.
uint32_t t_refract
The refractory timer countdown value.
input_t inputs[2]
The inputs to add in the next timestep.
mars_kiss64_seed_t random_seed
The random state.
REAL bias
The bias value.
uint32_t refract_timer
The refractory timer.
definition of neuron state
REAL bias
The bias value.
mars_kiss64_seed_t random_seed
Random seed to use.
UREAL tau_ms
The tau value of the neuron.
UREAL time_step
The timestep of the neuron being used.
uint32_t refract_init
The initial refractory timer.
definition of neuron parameters
Recording of the state of a neuron (spiking, voltage, etc.)
static void neuron_recording_record_accum(uint32_t var_index, uint32_t neuron_index, accum value)
stores a recording of an accum variable only; this is faster than neuron_recording_record_value for t...
static void neuron_recording_record_bit(uint32_t var_index, uint32_t neuron_index)
stores a recording of a set bit; this is the only way to set a bit in a bitfield; neuron_recording_re...
static void neuron_recording_record_int32(uint32_t var_index, uint32_t neuron_index, int32_t value)
stores a recording of an int32_t variable only; this is faster than neuron_recording_record_value for...
uint32_t mars_kiss64_seed(mars_kiss64_seed_t seed)
uint32_t mars_kiss64_seed_t[4]
void validate_mars_kiss64_seed(mars_kiss64_seed_t seed)
#define random
#define NULL
void spin1_memcpy(void *dst, void const *src, uint len)
Stochastic common code.
static uint32_t get_probability(UREAL tau, REAL p)
Calculates the probability as a uint32_t from 0 to 0xFFFFFFFF (which is 1)
static stdp_params params
Configuration parameters.