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Learning Spatiotemporally Encoded Pattern Transformations in Structured Spiking Neural Networks.

Gardner, B, Sporea, I and Grüning, A (2015) Learning Spatiotemporally Encoded Pattern Transformations in Structured Spiking Neural Networks. Neural Comput, 27 (12). pp. 2548-2586.

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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.

Item Type: Article
Authors :
NameEmailORCID
Gardner, Bb.gardner@surrey.ac.ukUNSPECIFIED
Sporea, IUNSPECIFIEDUNSPECIFIED
Grüning, AUNSPECIFIEDUNSPECIFIED
Date : December 2015
Identification Number : 10.1162/NECO_a_00790
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 13:46
Last Modified : 17 May 2017 15:12
URI: http://epubs.surrey.ac.uk/id/eprint/840275

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