A Survey of Encoding Techniques for Signal Processing in Spiking Neural Networks

• DOI: 10.1007/s11063-021-10562-2
• Publication Date: 2021-07-22T21:03:24Z
• Authors: Daniel Auge; Julian Hille; Etienne Mueller; Alois Knoll
• Keywords: ENCODE; SIGNAL (programming language); Implementation

Abstract Biologically inspired spiking neural networks are increasingly popular in the field of artificial intelligence due to their ability solve complex problems while being power efficient. They do so by leveraging timing discrete spikes as main information carrier. Though, industrial applications still lacking, partially because question how encode incoming data into spike events cannot be uniformly answered. In this paper, we summarise signal encoding schemes presented literature and propose a uniform nomenclature prevent vague usage ambiguous definitions. Therefore survey both, theoretical foundations well schemes. This work provides foundation gives an overview over different application-oriented implementations which utilise