Model of the Hippocampal Learning of Spatio-temporal Sequences View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2010

AUTHORS

Julien Hirel , Philippe Gaussier , Mathias Quoy

ABSTRACT

We propose a model of the hippocampus aimed at learning the timed association between subsequent sensory events. The properties of the neural network allow it to learn and predict the evolution of continuous rate-coded signals as well as the occurrence of transitory events, using both spatial and non-spatial information. The system is able to provide predictions based on the time trace of past sensory events. Performance of the neural network in the precise temporal learning of spatial and non-spatial signals is tested in a simulated experiment. The ability of the hippocampus proper to predict the occurrence of upcoming spatio-temporal events could play a crucial role in the carrying out of tasks requiring accurate time estimation and spatial localization. More... »

PAGES

345-351

References to SciGraph publications

  • 2003-06-18. Sequence learning using the neural coding in COMPUTATIONAL METHODS IN NEURAL MODELING
  • Book

    TITLE

    Artificial Neural Networks – ICANN 2010

    ISBN

    978-3-642-15824-7
    978-3-642-15825-4

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-15825-4_46

    DOI

    http://dx.doi.org/10.1007/978-3-642-15825-4_46

    DIMENSIONS

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