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Approximating Optimal Information Transmission using Local Hebbian Algorithms in a Double Feedback Loop

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ICANN ’93 (ICANN 1993)

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Abstract

Maximising mutual information (MI) under various constraints has been suggested as a goal for neural networks in a perceptual system. Networks using Hebbian algorithms have been found to be suitable for optimising MI with either input or output noise. In this paper we show that a double feedback loop network, using local Hebbian algorithms, can approximate the characteristics required for optimizing MI with both input and output noise. This represents a better approximation than simply orthonormalising the principal subspace.

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References

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Author information

Authors and Affiliations

  1. Department of Mathematics, Centre for Neural Networks, King’s College London, Strand, London, WC2R 2LS, UK

    Mark D. Plumbley

Authors
  1. Mark D. Plumbley
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Editor information

Editors and Affiliations

  1. Dutch Foundation for Neural Networks, University of Nijmegen, Geert Grooteplein 21, 6525 EZ, Nijmegen, The Netherlands

    Stan Gielen  & Bert Kappen  & 

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© 1993 Springer-Verlag London Limited

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Plumbley, M.D. (1993). Approximating Optimal Information Transmission using Local Hebbian Algorithms in a Double Feedback Loop. In: Gielen, S., Kappen, B. (eds) ICANN ’93. ICANN 1993. Springer, London. https://doi.org/10.1007/978-1-4471-2063-6_105

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  • DOI: https://doi.org/10.1007/978-1-4471-2063-6_105

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  • Publisher Name: Springer, London

  • Print ISBN: 978-3-540-19839-0

  • Online ISBN: 978-1-4471-2063-6

  • eBook Packages: Springer Book Archive

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