-
Notifications
You must be signed in to change notification settings - Fork 45
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Merge pull request #1384 from SpiNNakerManchester/IF_curr_delta_STDP_…
…test Add tests for new IF_curr_delta STDP binaries
- Loading branch information
Showing
1 changed file
with
223 additions
and
0 deletions.
There are no files selected for viewing
223 changes: 223 additions & 0 deletions
223
spynnaker_integration_tests/test_stdp/test_IF_curr_delta_stdp.py
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,223 @@ | ||
| # 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. | ||
|
|
||
| import numpy | ||
| import unittest | ||
| import pyNN.spiNNaker as sim | ||
| from spinnaker_testbase import BaseTestCase | ||
|
|
||
|
|
||
| class TestIFCurrDeltaSTDP(BaseTestCase): | ||
|
|
||
| def mad_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, 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 | ||
|
|
||
| sim.setup(timestep=timestep) | ||
|
|
||
| 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') | ||
|
|
||
| # Set up projections | ||
| static_synapse = sim.StaticSynapse(weight=spike_current, delay=1) | ||
| teaching_synapse = sim.StaticSynapse(weight=spike_current, delay=2) | ||
|
|
||
| # 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') | ||
|
|
||
| # 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') | ||
|
|
||
| # teacher -> output | ||
| sim.Projection(teacher_population, output_neuron, | ||
| sim.AllToAllConnector(allow_self_connections=True), | ||
| teaching_synapse, receptor_type='excitatory') | ||
|
|
||
| # 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) | ||
|
|
||
| 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') | ||
|
|
||
| sim.run(runtime) | ||
|
|
||
| weights_exc = injector_proj_exc.get(["weight"], "list") | ||
| weights_inh = injector_proj_inh.get(["weight"], "list") | ||
|
|
||
| print(weights_exc) | ||
| print(weights_inh) | ||
|
|
||
| sim.end() | ||
|
|
||
| self.assertTrue(numpy.allclose(weights_exc, weights_inh, rtol=0.001)) | ||
|
|
||
| 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 | ||
|
|
||
| sim.setup(timestep=timestep) | ||
|
|
||
| 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') | ||
|
|
||
| # Set up projections | ||
| static_synapse = sim.StaticSynapse(weight=spike_current, delay=1) | ||
| teaching_synapse = sim.StaticSynapse(weight=spike_current, delay=2) | ||
|
|
||
| # 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') | ||
|
|
||
| # 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') | ||
|
|
||
| # teacher -> output | ||
| sim.Projection(teacher_population, output_neuron, | ||
| sim.AllToAllConnector(allow_self_connections=True), | ||
| teaching_synapse, receptor_type='excitatory') | ||
|
|
||
| # 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) | ||
|
|
||
| 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') | ||
|
|
||
| sim.run(runtime) | ||
|
|
||
| weights_exc = injector_proj_exc.get(["weight"], "list") | ||
| weights_inh = injector_proj_inh.get(["weight"], "list") | ||
|
|
||
| print(weights_exc) | ||
| print(weights_inh) | ||
|
|
||
| sim.end() | ||
|
|
||
| self.assertTrue(numpy.allclose(weights_exc, weights_inh, rtol=0.001)) | ||
|
|
||
| def test_mad_pair_additive_delta(self): | ||
| self.runsafe(self.mad_pair_additive_delta) | ||
|
|
||
| def test_nearest_pair_additive_delta(self): | ||
| self.runsafe(self.nearest_pair_additive_delta) | ||
|
|
||
|
|
||
| if __name__ == '__main__': | ||
| unittest.main() |