test IF_curr_exp_stdp_mad_recurrent_pre_stochastic_multiplicative.aplx

Author: Christian-B

examples/extra_models_examples/stdp_mad_recurrent_pre_stochastic_multiplicative.py

@@ -0,0 +1,134 @@
+# Copyright (c) 2026 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.
+
+"""
+stdp_mad_recurrent_pre_stochastic_multiplicative
+"""
+import matplotlib.pyplot as plt
+import pyNN.spiNNaker as p
+from pyNN.utility.plotting import Figure, Panel
+# pylint: disable=wrong-spelling-in-comment
+
+p.setup(timestep=1.0, min_delay=1.0)
+p.set_number_of_neurons_per_core(p.IF_curr_exp, 100)
+
+nSourceNeurons = 1  # number of input (excitatory) neurons
+nExcitNeurons = 1   # number of excitatory neurons in the recurrent memory
+nInhibNeurons = 10  # number of inhibitory neurons in the recurrent memory
+nTeachNeurons = 1
+runTime = 3200
+
+cell_params_lif = {
+    'cm': 0.25,        # nF was 0.25
+    'i_offset': 0.0,
+    'tau_m': 10.0,
+    'tau_refrac': 2.0,
+    'tau_syn_E': 0.5,
+    'tau_syn_I': 0.5,
+    'v_reset': -70.0,
+    'v_rest': -70.0,
+    'v_thresh': -50.0}
+
+populations = list()
+projections = list()
+
+stimulus = 0
+inhib = 1
+excit = 2
+teacher = 3
+
+weight_to_force_firing = 15.0
+baseline_excit_weight = 2.0
+
+spikes0 = list()
+teachingSpikes = list()
+for i in range(runTime//40):
+    spikes0.append(i*40)
+for i in range(runTime//80):
+    teachingSpikes.append(i*40+5+120)
+
+arrayEntries = []
+for i in range(nSourceNeurons):
+    newEntry = []
+    for spike in spikes0:
+        newEntry.append(spike + i*40.0/100.0)
+    arrayEntries.append(newEntry)
+spikeArray = {'spike_times': arrayEntries}
+
+teachlist = list()
+for i in range(nSourceNeurons):
+    teachlist.append(teachingSpikes)
+teachingSpikeArray = {'spike_times': teachlist}
+populations.append(p.Population(nSourceNeurons,
+                                p.SpikeSourceArray(**spikeArray),
+                                label='excit_pop_ss_array'))       # 0
+populations.append(p.Population(nInhibNeurons,
+                                p.IF_curr_exp(**cell_params_lif),
+                                label='inhib_pop'))                # 1
+populations.append(p.Population(nExcitNeurons,
+                                p.IF_curr_exp(**cell_params_lif),
+                                label='excit_pop'))                # 2
+populations.append(p.Population(nTeachNeurons,
+                                p.SpikeSourceArray(**teachingSpikeArray),
+                                label='teaching_ss_array'))        # 3
+
+stdp_model = p.STDPMechanism(
+    timing_dependence=p.extra_models.RecurrentRule(
+        accumulator_depression=-6, accumulator_potentiation=3,
+        mean_pre_window=10.0, mean_post_window=10.0, dual_fsm=False,
+        A_plus=0.2, A_minus=0.2),
+    weight_dependence=p.MultiplicativeWeightDependence(w_min=0.0, w_max=16.0),
+    weight=baseline_excit_weight, delay=1)
+
+projections.append(
+    p.Projection(populations[stimulus], populations[excit],
+                 p.AllToAllConnector(), synapse_type=stdp_model))
+
+projections.append(
+    p.Projection(populations[teacher], populations[excit],
+                 p.OneToOneConnector(), receptor_type='excitatory',
+                 synapse_type=p.StaticSynapse(
+                     weight=weight_to_force_firing, delay=1)))
+
+populations[inhib].record(['v', 'spikes'])
+populations[excit].record(['v', 'spikes'])
+
+p.run(runTime)
+
+final_weights = projections[0].get('weight', 'list', with_address=False)
+print(f"Final weights: {final_weights}")
+
+v = populations[excit].get_data('v')
+spikes = populations[excit].get_data('spikes')
+vInhib = populations[inhib].get_data('v')
+spikesInhib = populations[inhib].get_data('spikes')
+
+Figure(
+    # plot of the neuron spike times
+    Panel(spikes.segments[0].spiketrains,
+          yticks=True, markersize=0.2, xlim=(0, runTime)),
+    # membrane potential of the neurons
+    Panel(v.segments[0].filter(name='v')[0],
+          ylabel="Membrane potential (mV)",
+          data_labels=[populations[excit].label], yticks=True,
+          xlim=(0, runTime), xticks=True),
+    title="Simple associative memory: spikes and membrane potential",
+    annotations=f"Simulated with {p.name()}"
+)
+plt.show()
+
+p.end()
+
+# combined binaries [
+# IF_curr_exp_stdp_mad_recurrent_pre_stochastic_multiplicative.aplx]

integration_tests/test_scripts.py

@@ -166,6 +166,10 @@ def test_examples_extra_models_examples_vogel_2011_vogels_2011_live_split(self):
     def test_examples_extra_models_examples_stdp_associative_memory(self):
         self.check_script("examples/extra_models_examples/stdp_associative_memory.py")
 
+    def test_examples_extra_models_examples_stdp_mad_recurrent_pre_stochastic_multiplicative(self):
+        self.check_script("examples/extra_models_examples/stdp_mad_recurrent_pre_stochastic_multiplicative.py")
+        self.check_binaries_used(["IF_curr_exp_stdp_mad_recurrent_pre_stochastic_multiplicative.aplx"])
+
     def test_examples_extra_models_examples_stdp_triplet(self):
         self.check_script("examples/extra_models_examples/stdp_triplet.py")