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Sequence Disambiguation with Synaptic Traces in Associative 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

Among the abilities that a sequence processing network should possess sequence disambiguation, that is, the ability to let temporal context information influence the evolution of the network dynamics, is one of the most important. In this work we propose an instance of the Bayesian Confidence Propagation Neural Network (BCPNN) that learns sequences with probabilistic associative learning and is able to disambiguate sequences with the use of synaptic traces (low pass filtered versions of the activity). We describe first how the BCPNN achieves both sequence recall and sequence learning from temporal input. Our main result is that the BCPNN network equipped with dynamical memory in the form of synaptic traces is capable of solving the sequence disambiguation problem in a reliable way. We characterize the relationship between the sequence disambiguation capabilities of the network and its dynamical parameters. Furthermore, we show that the inclusion of an additional fast synaptic trace greatly increases the network disambiguation capabilities.

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Authors and Affiliations

  1. Computational Brain Science Lab, KTH Royal Institute of Technology, Stockholm, Sweden

    Ramon H. Martinez, Oskar Kviman, Anders Lansner & Pawel Herman

  2. Mathematics Department, Stockholm University, Stockholm, Sweden

    Anders Lansner

Authors
  1. Ramon H. Martinez
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  2. Oskar Kviman
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  3. Anders Lansner
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  4. Pawel Herman
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Corresponding author

Correspondence to Ramon H. Martinez .

Editor information

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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Martinez, R.H., Kviman, O., Lansner, A., Herman, P. (2019). Sequence Disambiguation with Synaptic Traces in Associative 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_61

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  • DOI: https://doi.org/10.1007/978-3-030-30487-4_61

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30486-7

  • Online ISBN: 978-3-030-30487-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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