Eprop examples

.gitignore

@@ -8,3 +8,5 @@ __pycache__
 *application_generated_data_files/
 *reports/
 .mypy_cache/
+*big_with_labels/
+*graphs/

.ratexcludes

@@ -1,5 +1,6 @@
 **/*.colour_map
 **/*.col
+**/*.npy
 **/SpiNNUtils/**
 **/SpiNNMachine/**
 **/SpiNNMan/**

eprop_testing/__init__.py

@@ -0,0 +1,13 @@
+# Copyright (c) 2019 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.

eprop_testing/delta_synapse_test.py

@@ -0,0 +1,92 @@
+# Copyright (c) 2019 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 pyNN.spiNNaker as pynn
+import numpy as np
+import matplotlib.pyplot as plt
+from pyNN.random import NumpyRNG, RandomDistribution
+from pyNN.utility.plotting import Figure, Panel
+
+
+runtime = 100
+pynn.setup(1.0)
+
+neuron_params = {
+    "v": 0,
+    "i_offset": 0,
+    "v_rest": 0
+    }
+
+
+inp_spike_source = pynn.Population(2,
+                               pynn.SpikeSourceArray,
+                               {'spike_times': [10]},
+                               label='Spike Source')
+
+pseudo_rec_spike_source = pynn.Population(2,
+                               pynn.SpikeSourceArray,
+                               {'spike_times': [80]},
+                               label='Spike Source')
+
+neuron = pynn.Population(2,
+                         pynn.extra_models.EPropAdaptive(**neuron_params),
+                         label='eprop_pop')
+
+inp_proj = pynn.Projection(inp_spike_source, neuron,
+                       pynn.OneToOneConnector(),
+                       # note that delays are now fixed to one in terms of spikes,
+                       # but the synaptic word field indexes the synapse array
+                       pynn.StaticSynapse(weight=[-0.5, 10] , delay=[1, 1]),
+                       receptor_type='input_connections')
+
+
+rec_proj = pynn.Projection(pseudo_rec_spike_source, neuron,
+                       pynn.OneToOneConnector(),
+                       # note that delays are now fixed to one in terms of spikes,
+                       # but the synaptic word field indexes the synapse array
+                       pynn.StaticSynapse(weight=[0.5, -10] , delay=[1, 1]),
+                       receptor_type='recurrent_connections')
+
+
+
+neuron.record('all')
+
+pynn.run(runtime)
+
+res = neuron.get_data('all')
+weights = inp_proj.get('weight', 'list', with_address=False)
+delays = inp_proj.get('delay', 'list', with_address=False)
+
+for i in range(len(weights)):
+    print(weights[i], delays[i])
+
+Figure(
+    Panel(res.segments[0].filter(name='v')[0],
+          ylabel="Membrane potential (mV)",
+          data_labels=neuron.label, yticks=True, xlim=(0, runtime)),
+    Panel(res.segments[0].filter(name='gsyn_exc')[0],
+          ylabel="gsyn excitatory (mV)",
+          data_labels=neuron.label, yticks=True, xlim=(0, runtime)),
+    Panel(res.segments[0].filter(name='gsyn_inh')[0],
+          xlabel="Time (ms)", xticks=True,
+          ylabel="gsyn inhibitory (mV)",
+          data_labels=neuron.label, yticks=True, xlim=(0, runtime)),
+    Panel(res.segments[0].spiketrains,
+          ylabel="Output Spikes",
+          data_labels=neuron.label, yticks=True, xlim=(0, runtime)),
+    title="Single eprop neuron"
+)
+
+plt.show()
+
+pynn.end()