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Learning Beyond Finite Memory in Recurrent Networks of Spiking Neurons

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Advances in Natural Computation (ICNC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3611))

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Abstract

We investigate possibilities of inducing temporal structures without fading memory in recurrent networks of spiking neurons strictly operating in the pulse-coding regime. We extend the existing gradient-based algorithm for training feed-forward spiking neuron networks (SpikeProp [1]) to recurrent network topologies, so that temporal dependencies in the input stream are taken into account. It is shown that temporal structures with unbounded input memory specified by simple Moore machines (MM) can be induced by recurrent spiking neuron networks (RSNN). The networks are able to discover pulse-coded representations of abstract information processing states coding potentially unbounded histories of processed inputs.

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Authors and Affiliations

  1. School of Computer Science, University of Birmingham, Birmingham, B15 2TT, UK

    Peter Tiňo & Ashley Mills

Authors
  1. Peter Tiňo
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  2. Ashley Mills
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. School of Software, Sun Yat-Sen University, 510275, Guangzhou, China

    Ke Chen

  3. School of Computer Engineering, Nanyang Technological University, BLK N4, 2b-39, Nanyang Avenue, 639798, Singapore

    Yew Soon Ong

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© 2005 Springer-Verlag Berlin Heidelberg

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Tiňo, P., Mills, A. (2005). Learning Beyond Finite Memory in Recurrent Networks of Spiking Neurons. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_95

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  • DOI: https://doi.org/10.1007/11539117_95

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28325-6

  • Online ISBN: 978-3-540-31858-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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