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Interactive Control of Computational Power in a Model of the Basal Ganglia-Thalamocortical Circuit by a Supervised Attractor-Based Learning Procedure

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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

The attractor-based complexity of a Boolean neural network refers to its ability to discriminate among the possible input streams, by means of alternations between meaningful and spurious attractor dynamics. The higher the complexity, the greater the computational power of the network. The fine tuning of the interactivity – the network’s feedback output combined with the current input stream – can maintain a high degree of complexity within stable domains of the parameters’ space. In addition, the attractor-based complexity of the network is related to the degree of discrimination of specific input streams. We present a novel supervised attractor-based learning procedure aimed at achieving a maximal discriminability degree of a selected input stream. With a predefined target value of discriminability degree and in the absence of changes in the internal connectivity matrix of the network, the learning procedure updates solely the weights of the feedback projections. In a Boolean model of the basal ganglia-thalamocortical circuit, we show how the learning trajectories starting from different configurations can converge to final configurations associated with same high discriminability degree. We discuss the possibility that the limbic system may play the role of the interactive feedback to the network studied here.

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Authors and Affiliations

  1. Laboratoire d’économie Mathématique (LEMMA), Université Paris 2 – Panthéon-Assas, 4, Rue Blaise Desgoffe, 75006, Paris, France

    Jérémie Cabessa

  2. NeuroHeuristic Research Group, University of Lausanne, Quartier UNIL-Dorigny, 1015, Lausanne, Switzerland

    Alessandro E. P. Villa

Authors
  1. Jérémie Cabessa
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  2. Alessandro E. P. Villa
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Correspondence to Alessandro E. P. Villa .

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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., Villa, A.E.P. (2017). Interactive Control of Computational Power in a Model of the Basal Ganglia-Thalamocortical Circuit by a Supervised Attractor-Based Learning Procedure. 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_39

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  • DOI: https://doi.org/10.1007/978-3-319-68600-4_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68599-1

  • Online ISBN: 978-3-319-68600-4

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