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The Ideal Noisy Environment for Fast Neural Computation

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3971))

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Abstract

A central issue in computational neuroscience is to answer why neural systems can process information extremely fast. Here we investigate the effect of noise and the collaborative activity of a neural population on speeding up computation. We find that 1) when input noise is Poissonian, i.e., its variance is proportional to the mean, and 2) when the neural ensemble is initially at its stochastic equilibrium state, noise has the ‘best’ effect of accelerating computation, in the sense that the input strength is linearly encoded by the number of neurons firing in a short-time window, and that the neural system can use a simple strategy to read out the stimulus rapidly and accurately.

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Author information

Authors and Affiliations

  1. Department of Informatics, University of Sussex, UK

    Si Wu

  2. Department of Computer Science, Warwick University, UK

    Jianfeng Feng

  3. Lab. for Mathematical Neuroscience, RIKEN Brain Science Institute, Japan

    Shun-ichi Amari

Authors
  1. Si Wu
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  2. Jianfeng Feng
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  3. Shun-ichi Amari
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd., 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Wu, S., Feng, J., Amari, Si. (2006). The Ideal Noisy Environment for Fast Neural Computation. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11759966_1

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  • DOI: https://doi.org/10.1007/11759966_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34439-1

  • Online ISBN: 978-3-540-34440-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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