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International Conference on Artificial Neural Networks

ICANN 2018: Artificial Neural Networks and Machine Learning – ICANN 2018 pp 693–702Cite as

An STDP Rule for the Improvement and Stabilization of the Attractor Dynamics of the Basal Ganglia-Thalamocortical Network

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11141))

Abstract

The basal ganglia-thalamocortical (BGT) network has been investigated for many years, in particular in relation to disorders of the motor system and of the sleep-waking cycle. Its attractor dynamics is related to significant aspects of processing and coding of information, the most important of which being associative memories. The consideration of a simplified Boolean model of the BGT network allows for an exhaustive analysis of its attractor dynamics. In this context, it has been shown that both global and local changes in the synaptic weights could strongly influence the attractor-based complexity of the network. We propose a novel adaptive spike-timing dependent plasticity (STDP) rule which allows the network to improve and stabilize its attractor complexity during its computational process. The rule is based on an adaptive learning rate which varies according to the attractor dynamics that the network continuously visits.

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Authors and Affiliations

  1. Laboratoire d’économie mathématique et de microéconomie appliquée (LEMMA), University 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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Corresponding author

Correspondence to Jérémie Cabessa .

Editor information

Editors and Affiliations

  1. Czech Academy of Sciences, Prague 8, Czech Republic

    Věra Kůrková

  2. Open University of Cyprus, Latsia, Cyprus

    Yannis Manolopoulos

  3. CITEC Bielefeld University, Bielefeld, Germany

    Barbara Hammer

  4. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  5. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

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Cabessa, J., Villa, A.E.P. (2018). An STDP Rule for the Improvement and Stabilization of the Attractor Dynamics of the Basal Ganglia-Thalamocortical Network. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11141. Springer, Cham. https://doi.org/10.1007/978-3-030-01424-7_68

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  • DOI: https://doi.org/10.1007/978-3-030-01424-7_68

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01423-0

  • Online ISBN: 978-3-030-01424-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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