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The Super-Turing Computational Power of Interactive Evolving Recurrent Neural Networks

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Artificial Neural Networks and Machine Learning – ICANN 2013 (ICANN 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8131))

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Abstract

Understanding the dynamical and computational capabilities of neural models represents an issue of central importance. Here, we consider a model of first-order recurrent neural networks provided with the possibility to evolve over time and involved in a basic interactive and memory active computational paradigm. In this context, we prove that the so-called interactive evolving recurrent neural networks are computationally equivalent to interactive Turing machines with advice, hence capable of super-Turing potentialities. We further provide a precise characterisation of the ω-translations realised by these networks. Therefore, the consideration of evolving capabilities in a first-order neural model provides the potentiality to break the Turing barrier.

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Author information

Authors and Affiliations

  1. Department of Information Systems, University of Lausanne, CH-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. Faculty Automation,, Technical University of Sofia, 8 St. Kl. Ohridski Blvd., 1000, Sofia, Bulgaria

    Valeri Mladenov

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

    Petia Koprinkova-Hristova

  3. Institute of Neural Information Processing, University of Ulm, 89075, Ulm, Germany

    Günther Palm

  4. Quartier UNIL-Dorigny, Bâtiment Internef, Université de Lausanne, 1015, Lausanne, Switzerland

    Alessandro E. P. Villa

  5. Department of Computer Science, University of Milano, Via Comelico, 39, 20135, Milano, Italy

    Bruno Appollini

  6. Knowledge Engineering, School of Computing and Mathematical Sciences, Auckland University of Technology, 120 Mayoral Drive, 3rd floor, 1010, Auckland, New Zealand

    Nikola Kasabov

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Cabessa, J., Villa, A.E.P. (2013). The Super-Turing Computational Power of Interactive Evolving Recurrent Neural Networks. In: Mladenov, V., Koprinkova-Hristova, P., Palm, G., Villa, A.E.P., Appollini, B., Kasabov, N. (eds) Artificial Neural Networks and Machine Learning – ICANN 2013. ICANN 2013. Lecture Notes in Computer Science, vol 8131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40728-4_8

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  • DOI: https://doi.org/10.1007/978-3-642-40728-4_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40727-7

  • Online ISBN: 978-3-642-40728-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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