S4NN: temporal backpropagation for spiking neural networks with one spike per neuron

Understand

We propose a new supervised learning rule for multilayer spiking neural networks (SNNs) that use a form of temporal coding known as rank-order-coding.

• With this coding scheme, all neurons fire exactly one spike per stimulus, but the firing order carries information.
• In particular, in the readout layer, the first neuron to fire determines the class of the stimulus.
• We derive a new learning rule for this sort of network, named S4NN, akin to traditional error backpropagation, yet based on latencies.