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Interactive Evolving Recurrent Neural Networks Are Super-Turing Universal

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Artificial Neural Networks and Machine Learning – ICANN 2014 (ICANN 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8681))

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Abstract

Understanding the dynamical and computational capabilities of neural models represents an issue of central importance. In this context, recent results show that interactive evolving recurrent neural networks are super-Turing, irrespective of whether their synaptic weights are rational or real. We extend these results by showing that interactive evolving recurrent neural networks are not only super-Turing, but also capable of simulating any other possible interactive deterministic system. In this sense, interactive evolving recurrent neural networks represents a super-Turing universal model of computation, irrespective of whether their synaptic weights are rational or real.

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Author information

Authors and Affiliations

  1. Laboratory of Mathematical Economics (LEMMA), University of Paris 2 – Panthéon-Assas, 4 Rue Blaise Desgoffe, 75006, Paris, France

    Jérémie Cabessa

  2. Neuroheuristic Research Group, Department of Information Systems, University of Lausanne, 1015, Lausanne, Switzerland

    Jérémie Cabessa & Alessandro E. P. Villa

Authors
  1. Jérémie Cabessa
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  2. Alessandro E. P. Villa
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Editor information

Editors and Affiliations

  1. Department of Informatics, University of Hamburg, Vogt-Kölln-Straße 30, 22527, Hamburg, Germany

    Stefan Wermter , Cornelius Weber  & Sven Magg ,  & 

  2. Department of Informatics, Nicolaus Compernicus University, ul. Grudziądzka 5, 87-100, Torun, Poland

    Włodzisław Duch

  3. Department of Modern Languages, University of Helsinki, P.O. Box 24, 00014, Helsinki, Finland

    Timo Honkela

  4. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Acad. G. Bonchev str. bl. 25A, 1113, Sofia, Bulgaria

    Petia Koprinkova-Hristova

  5. Institute of Neural Information Processing, University of Ulm, 89069, Oberer Eselsberg, Ulm, Germany

    Günther Palm

  6. Department of Information Systems, Quartier UNIL-Dorigny, Bâtiment Internef, University of Lausanne, 1015, Lausanne, Switzerland

    Alessandro E. P. Villa

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Cabessa, J., Villa, A.E.P. (2014). Interactive Evolving Recurrent Neural Networks Are Super-Turing Universal. In: Wermter, S., et al. Artificial Neural Networks and Machine Learning – ICANN 2014. ICANN 2014. Lecture Notes in Computer Science, vol 8681. Springer, Cham. https://doi.org/10.1007/978-3-319-11179-7_8

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  • DOI: https://doi.org/10.1007/978-3-319-11179-7_8

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11178-0

  • Online ISBN: 978-3-319-11179-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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