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Neural Computation with Spiking Neural Networks Composed of Synfire Rings

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Artificial Neural Networks and Machine Learning – ICANN 2017 (ICANN 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10613))

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Abstract

We show that any finite state automaton can be simulated by some neural network of Izhikevich spiking neurons composed of interconnected synfire rings. The construction turns out to be robust to the introduction of two kinds of synaptic noises. These considerations show that a biological paradigm of neural computation based on sustained activities of cell assemblies is indeed possible.

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Notes

  1. 1.

    For the case of RS-IZH neurons, the exponential decay’s rate of the excitatory synapses has been changed from 0.3 to 0.4.

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Author information

Authors and Affiliations

  1. Laboratoire d’économie mathématique (LEMMA), Université Paris 2, Panthéon-Assas, Paris, France

    Jérémie Cabessa

  2. Équipe de Statistique Appliquée, PSL Research University, ESPCI-ParisTech, Paris, France

    Ginette Horcholle-Bossavit & Brigitte Quenet

  3. Neurophysiologie respiratoire expérimentale et clinique, Sorbonne Universités, UPMC Université Paris 6, INSERM, UMRS1158, Paris, France

    Ginette Horcholle-Bossavit & Brigitte Quenet

Authors
  1. Jérémie Cabessa
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  2. Ginette Horcholle-Bossavit
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  3. Brigitte Quenet
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Corresponding author

Correspondence to Jérémie Cabessa .

Editor information

Editors and Affiliations

  1. University of Lausanne, Lausanne, Switzerland

    Alessandra Lintas

  2. University of Genoa, Genoa, Italy

    Stefano Rovetta

  3. Universitat Pompeu Fabra, Barcelona, Spain

    Paul F.M.J. Verschure

  4. University of Lausanne, Lausanne, Switzerland

    Alessandro E.P. Villa

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Cabessa, J., Horcholle-Bossavit, G., Quenet, B. (2017). Neural Computation with Spiking Neural Networks Composed of Synfire Rings. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science(), vol 10613. Springer, Cham. https://doi.org/10.1007/978-3-319-68600-4_29

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  • DOI: https://doi.org/10.1007/978-3-319-68600-4_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68599-1

  • Online ISBN: 978-3-319-68600-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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