Spike-Event-Driven Deep Spiking Neural Network With Temporal Encoding
Zhixuan Zhang, Qi Liu
Abstract
Feature extractionplays an important role before pattern recognition takes place. The existing artificial neural networks (ANNs), however, ignoreto learn and represent temporal information, instead of only utilizing spatial information for recognition. Moreover, the substantial computational and energy costs resulted from the conventional ANN-based classifiers, limit their uses in mobile and embedded applications. In this work, we develop a sparse temporal encoding method which exploits both spatial and temporal information. On the basis of spike-timing-dependent plasticity and multi-scale structure, the resulting temporal feature representation integrates with a temporal spiking neural network (SNN) classifier to achieve high efficiency of parallel computing for feature extraction. Experimental evaluation on four benchmark datasets from image classification and speech recognition tasks show the proposed SNN model yielding state-of-the-art accuracy.