func TestMultipleConnectionSpikingNeuronNetwork(t *testing.T) {
// Create a group of neurons with the default parameters.
// Connect to a redis client.
redisConnection, err := redis.Dial("tcp", ":6379")
if err != nil {
// Handle error if any...
panic(err)
}
defer redisConnection.Close()
network := make([]*sn.SpikingNeuron, numberOfSpikingNeurons)
// Create a feed-forward network.
for i := 0; i < numberOfSpikingNeurons; i++ {
// Define the neuron.
network[i] = sn.NewSpikingNeuron(defaultA, defaultB, defaultC, defaultD, int64(i), redisConnection)
if i > 0 {
// Create a connection from one neuron to the next.
network[i-1].CreateConnection(network[i], defaultWeight, true, 0)
}
}
// This is a hacky way to create an external connection...
externalConnections := make([]*sn.SpikingNeuron, numberOfExternalConnections)
for index, _ := range externalConnections {
externalConnections[index] = sn.NewSpikingNeuron(0, 0, 0, 0, int64(-index), redisConnection)
externalConnections[index].CreateConnection(network[0], 1.0, true, 0)
}
// Create a default simulation.
simulation := sim.NewSimulation(defaultSteps, defaultTau, defaultStart, defaultStepRise)
var testNetwork *net.Network
for input := float64(0); input < maxInput+lazyIncrement; input = input + lazyIncrement {
if input == 0 {
input = 1
}
for _, externalSource := range externalConnections {
for _, connection := range externalSource.GetConnections() {
if connection.IsWriteable() {
connection.SetOutput(input)
}
}
}
// Create a new network simulation of connections feeding from/to neurons.
testNetwork = net.NewNetwork(network)
testNetwork.Simulate(simulation)
for plotIndex, neuron := range testNetwork.GetNeurons() {
plotIndexString := alphabet[plotIndex]
numberOfConnections := strconv.Itoa(len(neuron.GetConnections()))
inputString := strconv.FormatFloat(input, 'f', 6, 64)
title := "Phasic Spiking Neuron " + plotIndexString + " with connections " + numberOfConnections + " @ I = " + inputString
xLabel := "Time Series"
yLabel := "Membrane Potential"
legendLabel := "Membrane Potential over Time"
fileName := "plots/spiking-neuron-" + plotIndexString + "-" + inputString + "-with-" + numberOfConnections + "-connections.png"
plots.GeneratePlot(simulation.GetTimeSeries(), neuron.GetOutputs(), title, xLabel, yLabel, legendLabel, fileName)
neuron.SetSpikeRate(input, float64(neuron.GetSpikes())/defaultMeasureStart)
neuron.ResetParameters(defaultA, defaultB, defaultC, defaultD)
}
if input == 1 {
input = 0
}
}
}
// External Source -> Neuron A -> Neuron B -> Neuron C ... example.
func TestSingleConnectionSpikingNeuronNetwork(t *testing.T) {
// Create a group of neurons with the default parameters.
// Connect to a redis client.
redisConnection, err := redis.Dial("tcp", ":6379")
if err != nil {
// Handle error if any...
panic(err)
}
defer redisConnection.Close()
network := make([]*sn.SpikingNeuron, numberOfSpikingNeurons)
// Create a feed-forward network.
for i := 0; i < numberOfSpikingNeurons; i++ {
// Define the neuron.
network[i] = sn.NewSpikingNeuron(defaultA, defaultB, defaultC, defaultD, int64(i), redisConnection)
if i > 0 {
// Create a connection from one neuron to the next.
network[i-1].CreateConnection(network[i], defaultWeight, true, 0)
}
}
// This is a hacky way to create an external connection...
externalSource := sn.NewSpikingNeuron(0, 0, 0, 0, int64(-1), redisConnection)
externalSource.CreateConnection(network[0], 1.0, true, 0)
// Create a default simulation.
simulation := sim.NewSimulation(defaultSteps, defaultTau, defaultStart, defaultStepRise)
var testNetwork *net.Network
for input := float64(0); input < maxInput+lazyIncrement; input = input + lazyIncrement {
if input == 0 {
input = 1
}
for _, connection := range externalSource.GetConnections() {
if connection.IsWriteable() {
connection.SetOutput(input)
}
}
// Create a new network simulation of connections feeding from/to neurons.
testNetwork = net.NewNetwork(network)
testNetwork.Simulate(simulation)
for plotIndex, neuron := range testNetwork.GetNeurons() {
plotIndexString := alphabet[plotIndex]
inputString := strconv.FormatFloat(input, 'f', 6, 64)
title := "Phasic Spiking Neuron " + plotIndexString + " @ I = " + inputString
xLabel := "Time Series"
yLabel := "Membrane Potential"
legendLabel := "Membrane Potential over Time"
fileName := "plots/spiking-neuron-" + plotIndexString + "-" + inputString + ".png"
plots.GeneratePlot(simulation.GetTimeSeries(), neuron.GetOutputs(), title, xLabel, yLabel, legendLabel, fileName)
neuron.SetSpikeRate(input, float64(neuron.GetSpikes())/defaultMeasureStart)
neuron.ResetParameters(defaultA, defaultB, defaultC, defaultD)
}
if input == 1 {
input = 0
}
}
// Measure and plot out mean spike rates.
meansMap := make(map[int64][]float64)
for _, neuron := range testNetwork.GetNeurons() {
means := make([]float64, len(inputMeasurements))
for i, selectedInput := range inputMeasurements {
neuronSpikeRate := neuron.GetSpikeRate()
means[i] = neuronSpikeRate[selectedInput]
}
meansMap[neuron.GetId()] = means
}
plotIndex := int(0)
for _, value := range meansMap {
plotIndexString := alphabet[plotIndex]
title := "Mean Spike Rate of Phasic Spiking Neuron " + plotIndexString
xLabel := "Input"
yLabel := "Spike Rate"
legendLabel := "Spike Rate as a Function of Input"
fileName := "plots/spiking-neuron-network-mean-spike-rate-" + plotIndexString + ".png"
plots.GeneratePlot(inputMeasurements, value, title, xLabel, yLabel, legendLabel, fileName)
plotIndex++
}
}