Poutré. Spike-prop: backpropagation for networks of spiking neurons

Abstract. For a network of spiking neurons with reasonable post-synaptic potentials, we derive a supervised learning rule akin to tradi-tional error-back-propagation, SpikeProp and show how to overcome the discontinuities introduced by thresholding. Using this learning algorithm, we demonstrate how networks of spiking neurons with biologically plau-sible time-constants can perform complex non-linear classication in fast temporal coding just as well as rate-coded networks. When comparing the (implicit) number of neurons required for the respective encodings, it is empirically demonstrated that temporal coding potentially requires signi cantly less neurons.