Spike-event-driven Deep Spiking Neural Network with Temporal Encoding

2021 
Feature extraction plays an important role before pattern recognition takes place. The existing artificial neural networks (ANNs), however, ignore to 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 use 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-based model yields state-of-the-art accuracy.
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