Building a Bridge Between Spiking and Artificial Neural Networks

Abstract

Spiking neural networks (SNN) are a promising approach for the detection of patterns with a temporal component. However they provide more parameters than conventional artificial neural networks (ANN) which make them hard to handle. Many error-gradient-based approaches work with a time-to-first-spike code because the explicit calculation of a gradient in SNN is - due to the nature of spikes - very difficult. In this paper, we present the estimation of such an error-gradient based on the gain function of the neurons. This is done by interpreting spike trains as rate codes in a given time interval. This way a bridge is built between SNN and ANN. This bridge allows us to train the SNN with the well-known error back-propagation algorithm for ANN.KeywordsSpike TrainRate CodeSpike RateGain FunctionSpike CountThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.