From c21d4264a7a576d31a519ff9eadbdb2e57e974f5 Mon Sep 17 00:00:00 2001
From: Andrew Rowley <Andrew.Rowley@manchester.ac.uk>
Date: Tue, 24 Sep 2024 09:00:56 +0100
Subject: [PATCH] WTA connector example

---
 examples/wta_example.py | 70 +++++++++++++++++++++++++++++++++++++++++
 1 file changed, 70 insertions(+)
 create mode 100644 examples/wta_example.py

diff --git a/examples/wta_example.py b/examples/wta_example.py
new file mode 100644
index 0000000..3af66d7
--- /dev/null
+++ b/examples/wta_example.py
@@ -0,0 +1,70 @@
+# 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.
+
+# Demonstration of the WTA connector in use.  There are two populations both
+# receiving input from the same Poisson source.  One population has a
+# self-connection with a WTA connector, which will attempt to ensure that only
+# one neuron in the population spikes at a time.  As neuron 2 has a higher rate
+# of input than the others, it will be the "winner" more often than the others.
+
+# Note that SpiNNaker does not send "instantaneous" spikes, so there can be
+# times where two neurons spike in the same time step.
+
+# The output graph shows the difference in the outputs of the two populations.
+
+import pyNN.spiNNaker as sim
+import matplotlib.pyplot as plt
+import numpy
+
+sim.setup(1.0)
+
+pop = sim.Population(10, sim.IF_curr_exp(), label="pop")
+wta = sim.Population(10, sim.IF_curr_exp(), label="wta")
+stim = sim.Population(
+    10, sim.SpikeSourcePoisson(
+        rate=[10, 10, 20, 10, 10, 10, 10, 10, 10, 10]),
+    label="stim")
+pop.record("spikes")
+wta.record("spikes")
+
+sim.Projection(
+    stim, pop, sim.OneToOneConnector(), sim.StaticSynapse(weight=5.0))
+sim.Projection(
+    stim, wta, sim.OneToOneConnector(), sim.StaticSynapse(weight=5.0))
+sim.Projection(
+    wta, wta, sim.extra_models.WTAConnector(), sim.StaticSynapse(weight=10.0),
+    receptor_type="inhibitory")
+
+sim.run(10000)
+
+pop_spikes = pop.get_data("spikes").segments[0].spiketrains
+wta_spikes = wta.get_data("spikes").segments[0].spiketrains
+
+sim.end()
+
+# Plot the spikes
+for spiketrain in pop_spikes:
+    y = numpy.ones_like(spiketrain) * spiketrain.annotations["source_index"]
+    line, = plt.plot(spiketrain, y.magnitude * 2, "r|",
+                     label="Without WTA")
+for spiketrain in wta_spikes:
+    y = numpy.ones_like(spiketrain) * spiketrain.annotations["source_index"]
+    line_2, = plt.plot(spiketrain, (y.magnitude * 2) + 1, "b|",
+                       label="With WTA")
+plt.xlabel("Time (ms)")
+plt.title("Simple example")
+plt.legend(handles=[line, line_2], loc=9)
+plt.ylim(-2, 24)
+plt.yticks([], [])
+plt.show()