/**
* Test the behaviour of a synapse model.
*
* @param network A neural network containing two neurons and a single synapse to be tested. The
* neurons at index 0 and 1 should be the pre- and post-synaptic neurons respectively, and are
* typically configured to produce a fixed firing pattern (though any neuron type and firing
* pattern are permitted).
* @param simSteps The number of steps to run the simulation for.
* @param logSpikesAndStateVariables Whether to record pre- and post-synaptic spikes and any state
* variables exposed by the synapse model in the test results.
* @param simStepsNoSpikes The number of steps to run the simulation for with no spiking after the
* normal spike test. This is useful for testing models which change the efficacy in the
* absence of spikes.
* @return For a single spike protocol, a TestResults object with type {@link TYPE#STDP}
* consisting of series labelled "Time" and "Efficacy" and if logSpikesAndStateVariables ==
* true then also "Pre-synaptic spikes", "Post-synaptic spikes" and any state variables
* exposed by the synapse model.
*/
public static TestResults singleTest(
NeuralNetwork network,
long simSteps,
boolean logSpikesAndStateVariables,
long simStepsNoSpikes) {
if (network.getNeurons().getSize() != 2 || network.getSynapses().getSize() != 1) {
throw new IllegalArgumentException(
"The neural network must contain at least 2 neurons and 1 synapse.");
}
simStepsNoSpikes = Math.max(0, simStepsNoSpikes);
int displayTimeResolution = Math.min(1000, network.getTimeResolution());
int logStepCount = network.getTimeResolution() / displayTimeResolution;
int logSize = (int) ((simSteps + simStepsNoSpikes) / logStepCount);
double[] timeLog = new double[logSize];
double[] efficacyLog = new double[logSize];
double[][] prePostLogs = null, traceLogs = null;
double[] stateVars;
if (logSpikesAndStateVariables) {
prePostLogs = new double[2][logSize];
traceLogs = new double[network.getSynapses().getStateVariableNames().length][logSize];
}
int logIndex = 0;
long step = 0;
for (; step < simSteps; step++) {
double time = network.getTime();
network.step();
if (step % logStepCount == 0) {
timeLog[logIndex] = time;
efficacyLog[logIndex] = network.getSynapses().getEfficacy(0);
// If we're only testing a few repetitions, include some extra
// data.
if (logSpikesAndStateVariables) {
prePostLogs[0][logIndex] = network.getNeurons().getOutput(0);
prePostLogs[1][logIndex] = network.getNeurons().getOutput(1);
stateVars = network.getSynapses().getStateVariableValues(0);
for (int v = 0; v < stateVars.length; v++) {
traceLogs[v][logIndex] = stateVars[v];
}
}
logIndex++;
}
}
if (simStepsNoSpikes > 0) {
FixedProtocolNeuronConfiguration config =
new FixedProtocolNeuronConfiguration(100, new double[] {});
network.getNeurons().setConfiguration(0, config);
network.getNeurons().setComponentConfiguration(0, 0);
network.getNeurons().setComponentConfiguration(1, 0);
for (; step < simSteps + simStepsNoSpikes; step++) {
double time = network.getTime();
network.step();
if (step % logStepCount == 0) {
timeLog[logIndex] = time;
efficacyLog[logIndex] = network.getSynapses().getEfficacy(0);
// If we're only testing a few repetitions, include some extra data.
if (logSpikesAndStateVariables) {
prePostLogs[0][logIndex] = network.getNeurons().getOutput(0);
prePostLogs[1][logIndex] = network.getNeurons().getOutput(1);
stateVars = network.getSynapses().getStateVariableValues(0);
for (int v = 0; v < stateVars.length; v++) {
traceLogs[v][logIndex] = stateVars[v];
}
}
logIndex++;
}
}
}
TestResults results = new TestResults();
results.setProperty("type", TYPE.STDP);
results.addResult("Efficacy", efficacyLog);
results.addResult("Time", timeLog);
if (logSpikesAndStateVariables) {
results.addResult("Pre-synaptic spikes", prePostLogs[0]);
results.addResult("Post-synaptic spikes", prePostLogs[1]);
String[] stateVariableNames = network.getSynapses().getStateVariableNames();
for (int v = 0; v < stateVariableNames.length; v++) {
results.addResult(stateVariableNames[v], traceLogs[v]);
}
}
return results;
}
