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Neural Comput. 2015 Dec;27(12):2548-86. doi: 10.1162/NECO_a_00790. Epub 2015 Oct 23.

Learning Spatiotemporally Encoded Pattern Transformations in Structured Spiking Neural Networks.

Author information

1
Department of Computer Science, University of Surrey, Guildford, GU2 7XH, U.K. b.gardner@surrey.ac.uk.
2
Department of Computer Science, University of Surrey, Guildford, GU2 7XH, U.K. i.sporea@surrey.ac.uk.
3
Department of Computer Science, University of Surrey, Guildford, GU2 7XH, U.K. a.gruning@surrey.ac.uk.

Abstract

Information encoding in the nervous system is supported through the precise spike timings of neurons; however, an understanding of the underlying processes by which such representations are formed in the first place remains an open question. Here we examine how multilayered networks of spiking neurons can learn to encode for input patterns using a fully temporal coding scheme. To this end, we introduce a new supervised learning rule, MultilayerSpiker, that can train spiking networks containing hidden layer neurons to perform transformations between spatiotemporal input and output spike patterns. The performance of the proposed learning rule is demonstrated in terms of the number of pattern mappings it can learn, the complexity of network structures it can be used on, and its classification accuracy when using multispike-based encodings. In particular, the learning rule displays robustness against input noise and can generalize well on an example data set. Our approach contributes to both a systematic understanding of how computations might take place in the nervous system and a learning rule that displays strong technical capability.

PMID:
26496039
DOI:
10.1162/NECO_a_00790
[Indexed for MEDLINE]

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