Synaptic learning rules for sequence learning

  1. Eric Torsten Reifenstein  Is a corresponding author
  2. Ikhwan Bin Khalid
  3. Richard Kempter
  1. Institute of Theoretical Biology, Humboldt-Universität zu Berlin, Germany

Abstract

Remembering the temporal order of a sequence of events is a task easily performed by humans in everyday life, but the underlying neuronal mechanisms are unclear. This problem is particularly intriguing as human behavior often proceeds on a time scale of seconds, which is in stark contrast to the much faster millisecond time-scale of neuronal processing in our brains. One long-held hypothesis in sequence learning suggests that a particular temporal fine-structure of neuronal activity - termed 'phase precession' - enables the compression of slow behavioral sequences down to the fast time scale of the induction of synaptic plasticity. Using mathematical analysis and computer simulations, we find that - for short enough synaptic learning windows - phase precession can improve temporal-order learning tremendously and that the asymmetric part of the synaptic learning window is essential for temporal-order learning. To test these predictions, we suggest experiments that selectively alter phase precession or the learning window and evaluate memory of temporal order.

Data availability

Code and data are now available at https://gitlab.com/e.reifenstein/synaptic-learning-rules-for-sequence-learning

Article and author information

Author details

  1. Eric Torsten Reifenstein

    Department of Biology, Institute of Theoretical Biology, Humboldt-Universität zu Berlin, Berlin, Germany
    For correspondence
    eric@bccn-berlin.de
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6898-0178
  2. Ikhwan Bin Khalid

    Department of Biology, Institute of Theoretical Biology, Humboldt-Universität zu Berlin, Berlin, Germany
    Competing interests
    The authors declare that no competing interests exist.
  3. Richard Kempter

    Department of Biology, Institute of Theoretical Biology, Humboldt-Universität zu Berlin, Berlin, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5344-2983

Funding

Bundesministerium für Bildung und Forschung (01GQ1705)

  • Richard Kempter

Deutsche Forschungsgemeinschaft (GRK 1589/2,SPP 1665,SFB 1315)

  • Richard Kempter

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Martin Vinck, Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Germany

Publication history

  1. Received: February 3, 2021
  2. Accepted: March 31, 2021
  3. Accepted Manuscript published: April 16, 2021 (version 1)
  4. Version of Record published: June 3, 2021 (version 2)

Copyright

© 2021, Reifenstein et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Eric Torsten Reifenstein
  2. Ikhwan Bin Khalid
  3. Richard Kempter
(2021)
Synaptic learning rules for sequence learning
eLife 10:e67171.
https://doi.org/10.7554/eLife.67171

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