step = {
// Create the input and post-synaptic potential for the input
const inputLayer = new DigitInput();
const inputPSP = new PSP(inputLayer, 0.1);
// STDP-learned weights between input and excitatory layer
const stdp = new STDPWeights(DIGIT_SIZE, NUM_NEURONS);
// Create the excitatory neurons and post-synaptic potential for them
const excitatory_neurons = new NeuronCollection(() => new ALIF, NUM_NEURONS);
const exPSP = new PSP(excitatory_neurons, 0.2);
// Create the inhibitory neurons and post-synaptic potential for them
const inhibitory_neurons = new NeuronCollection(() => new LIF, NUM_NEURONS)
const inhPSP = new PSP(inhibitory_neurons, 0.2);
// These PSPs are used for our display code (they have nothing to do with
// the functionality of the network)
const display_tau = 0.2;
const inputDisplay = new PSP(inputLayer, display_tau); // For displaying input
const exDisplay = new PSP(excitatory_neurons, display_tau); // For displaying excitatory output
// Track the number of neurons that have fired
let numFiresForRound = 0;
inputLayer.onAdvance = () => numFiresForRound = 0; // When our input layer advances, reset our number of fires
return async function(t_step, t) {
await inputLayer.step(t_step, t); // Step the input
inputPSP.step(t_step); // Step input post-synaptic
const weightedInputVals = stdp.weightedOutput(inputPSP.output); // Compute the weighted inputs for the excitatory layer
// Compute the output of the inhibitory layer by...
const totalInhibitoryOutput = sum(inhPSP.output); // Finding the total inhibitory output
// And take the sum *minus* each inhibitory neuron's value to get every other inhibitory neuron's value
const inhibitoryOutputs = inhPSP.output.map((v) => totalInhibitoryOutput - v);
// Compute the input to the excitatory layer as the sum of the weighted input from the digit and the
// inhibitory input
const excitatoryInput = addArrays(weightedInputVals, mulArray(negArray(inhibitoryOutputs), 2));
excitatory_neurons.step(excitatoryInput, t_step); // Step excitatory layer
exPSP.step(t_step); // Step excitatory post-synaptic
inhibitory_neurons.step(excitatory_neurons.output, t_step); // Step inhibitory layer (using excitatory output as its input)
inhPSP.step(t_step); // Step inhibitory post-synaptic
stdp.step(t_step); // Step STDP
stdp.updateWeights(inputLayer.output, excitatory_neurons.output); // Update STDP weights
updateDisplays(t_step, inputDisplay, exDisplay, stdp, excitatory_neurons); // This will update the interactive displays
const numFires = excitatory_neurons.output.filter((val) => val > 0).length; // Number of neurons that fired in this timestep
numFiresForRound += numFires; // Total number of neurons that have fired since this digit has been displayed
if(numFiresForRound > 5) { // If enough neurons have fired...
inputLayer.canAdvance = true; // Then tell the input layer we can advance to the next digit
}
}
}
Purpose-built for displays of data
Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
