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Handbook of Natural Computing pp 335–376Cite as

Computing with Spiking Neuron Networks

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Abstract

Spiking Neuron Networks (SNNs) are often referred to as the third generation of neural networks. Highly inspired by natural computing in the brain and recent advances in neurosciences, they derive their strength and interest from an accurate modeling of synaptic interactions between neurons, taking into account the time of spike firing. SNNs overcome the computational power of neural networks made of threshold or sigmoidal units. Based on dynamic event-driven processing, they open up new horizons for developing models with an exponential capacity to memorize and a strong ability to do fast adaptation. Today, the main challenge is to discover efficient learning rules that might take advantage of the specific features of SNNs while keeping the nice properties (general-purpose, easy-to-use, available simulators, etc.) of traditional connectionist models. This chapter relates the history of the “spiking neuron” in Sect. 1 and summarizes the most currently-in-use models of neurons and synaptic plasticity in Sect. 2. The computational power of SNNs is addressed in Sect. 3 and the problem of learning in networks of spiking neurons is tackled in Sect. 4, with insights into the tracks currently explored for solving it. Finally, Sect. 5 discusses application domains, implementation issues and proposes several simulation frameworks.

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Authors and Affiliations

  1. Laboratoire LIRIS – CNRS, Université Lumière Lyon 2, Lyon, France

    Hélène Paugam-Moisy

  2. INRIA Saclay – lle-de-France, Université Paris-Sud, Orsay, France

    Hélène Paugam-Moisy

  3. Research Group Life Sciences, CWI, Amsterdam, The Netherlands

    Sander Bohte

Authors
  1. Hélène Paugam-Moisy
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  2. Sander Bohte
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Correspondence to Hélène Paugam-Moisy or Sander Bohte .

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Editors and Affiliations

  1. LIACS, Leiden University, Leiden, The Netherlands

    Grzegorz Rozenberg

  2. Computer Science Department, University of Colorado, Boulder, USA

    Grzegorz Rozenberg

  3. LIACS, Leiden University, Leiden, The Netherlands

    Thomas Bäck

  4. LIACS, Leiden University, Leiden, The Netherlands

    Joost N. Kok

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Paugam-Moisy, H., Bohte, S. (2012). Computing with Spiking Neuron Networks. In: Rozenberg, G., Bäck, T., Kok, J.N. (eds) Handbook of Natural Computing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92910-9_10

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