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Robust Optimal-Size Implementation of Finite State Automata with Synfire Ring-Based Neural Networks

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Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation (ICANN 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11727))

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Abstract

Synfire rings are important neural circuits capable of conveying synchronous, temporally precise and self-sustained activities in a robust manner. We describe a robust and optimal-size implementation of finite state automata with neural networks composed of synfire rings. More precisely, given any finite automaton, we build a corresponding neural network partly composed of synfire rings and capable of simulating it. The synfire ring activities encode the successive states of the automaton throughout its computation. The robustness of the network results from its architecture, which involves synfire rings and duplicated core components. We finally show that the network’s size is asymptotically optimal: for an automaton with n states, the network has \(\varTheta (\sqrt{n})\) cells.

Supports from DARPA – Lifelong Learning Machines (L2M) program, cooperative agreement No. HR0011-18-2-0023, as well as from the ICS CAS RVO: 67985807 and the Czech Science Foundation, grant No. 19-05704S, are gratefully acknowledged.

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Authors and Affiliations

  1. Laboratory of Mathematical Economics and Applied Microeconomics, University Paris 2 – Panthéon-Assas, 4, Rue Blaise Desgoffe, 75006, Paris, France

    Jérémie Cabessa

  2. Institute of Computer Science, Czech Academy of Sciences, P. O. Box 5, 18207, Prague 8, Czech Republic

    Jérémie Cabessa & Jiří Šíma

Authors
  1. Jérémie Cabessa
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  2. Jiří Šíma
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Correspondence to Jérémie Cabessa .

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Editors and Affiliations

  1. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Igor V. Tetko

  2. Institute of Computer Science, Czech Academy of Sciences, Prague 8, Czech Republic

    Věra Kůrková

  3. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Pavel Karpov

  4. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Fabian Theis

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Cabessa, J., Šíma, J. (2019). Robust Optimal-Size Implementation of Finite State Automata with Synfire Ring-Based Neural Networks. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation. ICANN 2019. Lecture Notes in Computer Science(), vol 11727. Springer, Cham. https://doi.org/10.1007/978-3-030-30487-4_62

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  • DOI: https://doi.org/10.1007/978-3-030-30487-4_62

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30486-7

  • Online ISBN: 978-3-030-30487-4

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