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28 changes: 28 additions & 0 deletions
28
neural_modelling/makefiles/neuron/IF_curr_delta_stdp_mad_nearest_pair_additive/Makefile
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| # 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. | ||
|
|
||
| APP = $(notdir $(CURDIR)) | ||
|
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| NEURON_MODEL_H = $(NEURON_DIR)/neuron/models/neuron_model_lif_impl.h | ||
| INPUT_TYPE_H = $(NEURON_DIR)/neuron/input_types/input_type_delta.h | ||
| NEURON_IMPL_H = $(NEURON_DIR)/neuron/implementations/neuron_impl_standard.h | ||
| THRESHOLD_TYPE_H = $(NEURON_DIR)/neuron/threshold_types/threshold_type_static.h | ||
| SYNAPSE_TYPE_H = $(NEURON_DIR)/neuron/synapse_types/synapse_types_delta_impl.h | ||
| SYNAPSE_DYNAMICS = $(NEURON_DIR)/neuron/plasticity/stdp/synapse_dynamics_stdp_mad_impl.c | ||
| TIMING_DEPENDENCE = $(NEURON_DIR)/neuron/plasticity/stdp/timing_dependence/timing_nearest_pair_impl.c | ||
| TIMING_DEPENDENCE_H = $(NEURON_DIR)/neuron/plasticity/stdp/timing_dependence/timing_nearest_pair_impl.h | ||
| WEIGHT_DEPENDENCE = $(NEURON_DIR)/neuron/plasticity/stdp/weight_dependence/weight_additive_one_term_impl.c | ||
| WEIGHT_DEPENDENCE_H = $(NEURON_DIR)/neuron/plasticity/stdp/weight_dependence/weight_additive_one_term_impl.h | ||
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| include ../neural_build.mk |
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neural_modelling/makefiles/neuron/IF_curr_delta_stdp_mad_pair_additive/Makefile
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| # 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. | ||
|
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||
| APP = $(notdir $(CURDIR)) | ||
|
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| NEURON_MODEL_H = $(NEURON_DIR)/neuron/models/neuron_model_lif_impl.h | ||
| INPUT_TYPE_H = $(NEURON_DIR)/neuron/input_types/input_type_delta.h | ||
| NEURON_IMPL_H = $(NEURON_DIR)/neuron/implementations/neuron_impl_standard.h | ||
| THRESHOLD_TYPE_H = $(NEURON_DIR)/neuron/threshold_types/threshold_type_static.h | ||
| SYNAPSE_TYPE_H = $(NEURON_DIR)/neuron/synapse_types/synapse_types_delta_impl.h | ||
| SYNAPSE_DYNAMICS = $(NEURON_DIR)/neuron/plasticity/stdp/synapse_dynamics_stdp_mad_impl.c | ||
| TIMING_DEPENDENCE = $(NEURON_DIR)/neuron/plasticity/stdp/timing_dependence/timing_pair_impl.c | ||
| TIMING_DEPENDENCE_H = $(NEURON_DIR)/neuron/plasticity/stdp/timing_dependence/timing_pair_impl.h | ||
| WEIGHT_DEPENDENCE = $(NEURON_DIR)/neuron/plasticity/stdp/weight_dependence/weight_additive_one_term_impl.c | ||
| WEIGHT_DEPENDENCE_H = $(NEURON_DIR)/neuron/plasticity/stdp/weight_dependence/weight_additive_one_term_impl.h | ||
|
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| include ../neural_build.mk |
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223 changes: 223 additions & 0 deletions
223
spynnaker_integration_tests/test_stdp/test_IF_curr_delta_stdp.py
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| # Copyright (c) 2023 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. | ||
|
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||
| import numpy | ||
| import unittest | ||
| import pyNN.spiNNaker as sim | ||
| from spinnaker_testbase import BaseTestCase | ||
|
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| class TestIFCurrDeltaSTDP(BaseTestCase): | ||
|
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| def mad_pair_additive_delta(self): | ||
| timestep = 1 | ||
| spike_current = 20 | ||
| input_population_size = 2 | ||
|
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| # Spikes set so the calculated weights for each STDP projection | ||
| # should be identical | ||
| spike_times1 = [[1, 27, 55], [1, 27, 55]] | ||
| spike_times2 = [[1, 33, 59], [1, 33, 59]] | ||
| training_times = [28] | ||
| save_spike_times = [90] | ||
| runtime = save_spike_times[-1] + 10 | ||
|
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| sim.setup(timestep=timestep) | ||
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| IF_curr_delta_model = sim.IF_curr_delta() | ||
|
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| # Set up populations | ||
| ssa1 = sim.Population( | ||
| input_population_size, sim.SpikeSourceArray(spike_times1), | ||
| label='ssa1') | ||
| ssa2 = sim.Population( | ||
| input_population_size, sim.SpikeSourceArray(spike_times2), | ||
| label='ssa2') | ||
| save_neuron = sim.Population( | ||
| 1, sim.SpikeSourceArray(save_spike_times), label='save_neuron') | ||
| injector_neurons_exc = sim.Population( | ||
| input_population_size, IF_curr_delta_model, | ||
| label='injector_neurons_exc') | ||
| injector_neurons_inh = sim.Population( | ||
| input_population_size, IF_curr_delta_model, | ||
| label='injector_neurons_inh') | ||
| teacher_population = sim.Population( | ||
| 1, sim.SpikeSourceArray(training_times), | ||
| label='teacher_population') | ||
| output_neuron = sim.Population( | ||
| 1, IF_curr_delta_model, label='output_neuron') | ||
|
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||
| # Set up projections | ||
| static_synapse = sim.StaticSynapse(weight=spike_current, delay=1) | ||
| teaching_synapse = sim.StaticSynapse(weight=spike_current, delay=2) | ||
|
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| # SSA -> injectors | ||
| sim.Projection( | ||
| ssa1, injector_neurons_exc, sim.OneToOneConnector(), | ||
| static_synapse, receptor_type='excitatory') | ||
| sim.Projection( | ||
| ssa2, injector_neurons_inh, sim.OneToOneConnector(), | ||
| static_synapse, receptor_type='excitatory') | ||
|
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| # save -> injectors | ||
| sim.Projection(save_neuron, injector_neurons_exc, | ||
| sim.AllToAllConnector(allow_self_connections=True), | ||
| static_synapse, receptor_type='excitatory') | ||
| sim.Projection(save_neuron, injector_neurons_inh, | ||
| sim.AllToAllConnector(allow_self_connections=True), | ||
| static_synapse, receptor_type='excitatory') | ||
|
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| # teacher -> output | ||
| sim.Projection(teacher_population, output_neuron, | ||
| sim.AllToAllConnector(allow_self_connections=True), | ||
| teaching_synapse, receptor_type='excitatory') | ||
|
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| # stdp models for injector -> output | ||
| stdp_model = sim.STDPMechanism( | ||
| timing_dependence=sim.SpikePairRule( | ||
| tau_plus=10, tau_minus=12, A_plus=1, A_minus=-1), | ||
| weight_dependence=sim.AdditiveWeightDependence(w_min=0, w_max=20), | ||
| weight=0, delay=1) | ||
| stdp_model2 = sim.STDPMechanism( | ||
| timing_dependence=sim.SpikePairRule( | ||
| tau_plus=10, tau_minus=12, A_plus=1, A_minus=-1), | ||
| weight_dependence=sim.AdditiveWeightDependence(w_min=0, w_max=20), | ||
| weight=0, delay=1) | ||
|
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| injector_proj_exc = sim.Projection( | ||
| injector_neurons_exc, output_neuron, | ||
| sim.AllToAllConnector(allow_self_connections=True), | ||
| stdp_model, receptor_type='excitatory') | ||
| injector_proj_inh = sim.Projection( | ||
| injector_neurons_inh, output_neuron, | ||
| sim.AllToAllConnector(allow_self_connections=True), | ||
| stdp_model2, receptor_type='inhibitory') | ||
|
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| sim.run(runtime) | ||
|
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| weights_exc = injector_proj_exc.get(["weight"], "list") | ||
| weights_inh = injector_proj_inh.get(["weight"], "list") | ||
|
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| print(weights_exc) | ||
| print(weights_inh) | ||
|
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| sim.end() | ||
|
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| self.assertTrue(numpy.allclose(weights_exc, weights_inh, rtol=0.001)) | ||
|
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| def nearest_pair_additive_delta(self): | ||
| timestep = 1 | ||
| spike_current = 20 | ||
| input_population_size = 2 | ||
|
|
||
| # Spikes set so the calculated weights for each STDP projection | ||
| # should be identical | ||
| spike_times1 = [[1, 27, 51], [1, 27, 51]] | ||
| spike_times2 = [[1, 33, 60], [1, 33, 60]] | ||
| training_times = [28] | ||
| save_spike_times = [90] | ||
| runtime = save_spike_times[-1] + 10 | ||
|
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| sim.setup(timestep=timestep) | ||
|
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| IF_curr_delta_model = sim.IF_curr_delta() | ||
|
|
||
| # Set up populations | ||
| ssa1 = sim.Population( | ||
| input_population_size, sim.SpikeSourceArray(spike_times1), | ||
| label='ssa1') | ||
| ssa2 = sim.Population( | ||
| input_population_size, sim.SpikeSourceArray(spike_times2), | ||
| label='ssa2') | ||
| save_neuron = sim.Population( | ||
| 1, sim.SpikeSourceArray(save_spike_times), label='save_neuron') | ||
| injector_neurons_exc = sim.Population( | ||
| input_population_size, IF_curr_delta_model, | ||
| label='injector_neurons_exc') | ||
| injector_neurons_inh = sim.Population( | ||
| input_population_size, IF_curr_delta_model, | ||
| label='injector_neurons_inh') | ||
| teacher_population = sim.Population( | ||
| 1, sim.SpikeSourceArray(training_times), | ||
| label='teacher_population') | ||
| output_neuron = sim.Population( | ||
| 1, IF_curr_delta_model, label='output_neuron') | ||
|
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||
| # Set up projections | ||
| static_synapse = sim.StaticSynapse(weight=spike_current, delay=1) | ||
| teaching_synapse = sim.StaticSynapse(weight=spike_current, delay=2) | ||
|
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| # SSA -> injectors | ||
| sim.Projection( | ||
| ssa1, injector_neurons_exc, sim.OneToOneConnector(), | ||
| static_synapse, receptor_type='excitatory') | ||
| sim.Projection( | ||
| ssa2, injector_neurons_inh, sim.OneToOneConnector(), | ||
| static_synapse, receptor_type='excitatory') | ||
|
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||
| # save -> injectors | ||
| sim.Projection(save_neuron, injector_neurons_exc, | ||
| sim.AllToAllConnector(allow_self_connections=True), | ||
| static_synapse, receptor_type='excitatory') | ||
| sim.Projection(save_neuron, injector_neurons_inh, | ||
| sim.AllToAllConnector(allow_self_connections=True), | ||
| static_synapse, receptor_type='excitatory') | ||
|
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| # teacher -> output | ||
| sim.Projection(teacher_population, output_neuron, | ||
| sim.AllToAllConnector(allow_self_connections=True), | ||
| teaching_synapse, receptor_type='excitatory') | ||
|
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||
| # stdp models for injector -> output | ||
| stdp_model = sim.STDPMechanism( | ||
| timing_dependence=sim.extra_models.SpikeNearestPairRule( | ||
| tau_plus=10, tau_minus=12, A_plus=1, A_minus=-1), | ||
| weight_dependence=sim.AdditiveWeightDependence(w_min=0, w_max=20), | ||
| weight=0, delay=1) | ||
| stdp_model2 = sim.STDPMechanism( | ||
| timing_dependence=sim.extra_models.SpikeNearestPairRule( | ||
| tau_plus=10, tau_minus=12, A_plus=1, A_minus=-1), | ||
| weight_dependence=sim.AdditiveWeightDependence(w_min=0, w_max=20), | ||
| weight=0, delay=1) | ||
|
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| injector_proj_exc = sim.Projection( | ||
| injector_neurons_exc, output_neuron, | ||
| sim.AllToAllConnector(allow_self_connections=True), | ||
| stdp_model, receptor_type='excitatory') | ||
| injector_proj_inh = sim.Projection( | ||
| injector_neurons_inh, output_neuron, | ||
| sim.AllToAllConnector(allow_self_connections=True), | ||
| stdp_model2, receptor_type='inhibitory') | ||
|
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| sim.run(runtime) | ||
|
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| weights_exc = injector_proj_exc.get(["weight"], "list") | ||
| weights_inh = injector_proj_inh.get(["weight"], "list") | ||
|
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| print(weights_exc) | ||
| print(weights_inh) | ||
|
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| sim.end() | ||
|
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| self.assertTrue(numpy.allclose(weights_exc, weights_inh, rtol=0.001)) | ||
|
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| def test_mad_pair_additive_delta(self): | ||
| self.runsafe(self.mad_pair_additive_delta) | ||
|
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| def test_nearest_pair_additive_delta(self): | ||
| self.runsafe(self.nearest_pair_additive_delta) | ||
|
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|
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| if __name__ == '__main__': | ||
| unittest.main() |