swMATH ID: 41327
Software Authors: Saeed Reza Kheradpisheh, Timothée Masquelier
Description: S4NN: temporal backpropagation for spiking neural networks with one spike per neuron. 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. We show how approximated error gradients can be computed backward in a feedforward network with any number of layers. This approach reaches state-of-the-art performance with supervised multi fully-connected layer SNNs: test accuracy of 97.4
Homepage: https://arxiv.org/abs/1910.09495
Source Code:  https://github.com/SRKH/S4NN
Related Software: Keras; TensorFlow; Matplotlib
Cited in: 1 Publication

Cited in 1 Serial

1 Neural Computation

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