Added example for collab submission

synfire/synfire_collab.py

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+"""
+Synfire chain example
+"""
+import spynnaker8 as sim
+from spynnaker8.utilities import neo_convertor
+
+# number of neurons in each population
+n_neurons = 100
+n_populations = 10
+weights = 0.5
+delays = 17.0
+simtime = 1000
+
+sim.setup(timestep=1.0, min_delay=1.0, max_delay=144.0)
+
+spikeArray = {'spike_times': [[0]]}
+stimulus = sim.Population(1, sim.SpikeSourceArray, spikeArray,
+                          label='stimulus')
+
+chain_pops = [
+    sim.Population(n_neurons, sim.IF_curr_exp, {}, label='chain_{}'.format(i))
+    for i in range(n_populations)
+]
+for pop in chain_pops:
+    pop.record("spikes")
+
+connector = sim.FixedNumberPreConnector(10)
+for i in range(n_populations):
+    sim.Projection(chain_pops[i], chain_pops[(i + 1) % n_populations],
+                   connector,
+                   synapse_type=sim.StaticSynapse(weight=weights,
+                                                  delay=delays))
+
+sim.Projection(stimulus, chain_pops[0], sim.AllToAllConnector(),
+               synapse_type=sim.StaticSynapse(weight=5.0))
+
+sim.run(simtime)
+# None PyNN method which is faster
+# spikes = [pop.spinnaker_get_data("spikes") for pop in chain_pops]
+
+# Pynn method and support method
+neos = [pop.get_data("spikes") for pop in chain_pops]
+spikes = map(neo_convertor.convert_spikes, neos)
+
+sim.end()
+
+
+if __name__ == '__main__':
+    try:
+        import pylab
+        pylab.figure()
+        pylab.xlabel('Time (ms)')
+        pylab.ylabel('Neuron')
+        pylab.title('Spikes Sent By Chain')
+        offset = 0
+        for pop_spikes in spikes:
+            pylab.plot(
+                [i[1] for i in pop_spikes],
+                [i[0] + offset for i in pop_spikes], "."
+            )
+            offset += n_neurons
+        pylab.savefig("results.png")
+    except Exception as ex:
+        print ex
+        print spikes