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

• Published in: Neural processing letters Vol. 53; no. 6; pp. 4693 - 4710
• Main Authors: Auge, Daniel, Hille, Julian, Mueller, Etienne, Knoll, Alois
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2021; Springer Nature B.V
• Subjects: Artificial Intelligence; Codes; Complex Systems; Computational Intelligence; Computer Science; Energy consumption; Industrial applications; Neural networks; Neurons; Signal encoding; Signal processing; Spiking; Taxonomy; Neural coding; Rate coding; Temporal coding; Spiking neural networks; Neuromorphic computing
• ISSN: 1370-4621; 1573-773X
• DOI: 10.1007/s11063-021-10562-2

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