A model of hippocampal replay driven by experience and environmental structure facilitates spatial learning

  1. Nicolas Diekmann
  2. Sen Cheng  Is a corresponding author
  1. Ruhr University Bochum, Germany

Abstract

Replay of neuronal sequences in the hippocampus during resting states and sleep play an important role in learning and memory consolidation. Consistent with these functions, replay sequences have been shown to obey current spatial constraints. Nevertheless, replay does not necessarily reflect previous behavior and can construct never-experienced sequences. Here we propose a stochastic replay mechanism that prioritizes experiences based on three variables: 1. Experience strength, 2. experience similarity, and 3. inhibition of return. Using this prioritized replay mechanism to train reinforcement learning agents leads to far better performance than using random replay. Its performance is close to the state-of-the-art, but computationally intensive, algorithm by Mattar & Daw (2018). Importantly, our model reproduces diverse types of replay because of the stochasticity of the replay mechanism and experience-dependent differences between the three variables. In conclusion, a unified replay mechanism generates diverse replay statistics and is efficient in driving spatial learning.

Data availability

The current manuscript is a computational study, so no data have been generated for this manuscript. Modelling code has been made publicly available at https://github.com/sencheng/-Mechanisms-and-Functions-of-Hippocampal-Replay.

Article and author information

Author details

  1. Nicolas Diekmann

    Faculty of Computer Science, Ruhr University Bochum, Bochum, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3638-7617
  2. Sen Cheng

    Faculty of Computer Science, Ruhr University Bochum, Bochum, Germany
    For correspondence
    sen.cheng@rub.de
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6719-8029

Funding

Deutsche Forschungsgemeinschaft (419037518 - FOR 2812 P2)

  • Sen Cheng

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

Reviewing Editor

  1. Payam Piray, University of Southern California, United States

Version history

  1. Preprint posted: July 28, 2022 (view preprint)
  2. Received: July 29, 2022
  3. Accepted: March 13, 2023
  4. Accepted Manuscript published: March 14, 2023 (version 1)
  5. Version of Record published: April 5, 2023 (version 2)

Copyright

© 2023, Diekmann & Cheng

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.

Metrics

  • 2,203
    Page views
  • 265
    Downloads
  • 3
    Citations

Article citation count generated by polling the highest count across the following sources: PubMed Central, Crossref, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Nicolas Diekmann
  2. Sen Cheng
(2023)
A model of hippocampal replay driven by experience and environmental structure facilitates spatial learning
eLife 12:e82301.
https://doi.org/10.7554/eLife.82301

Share this article

https://doi.org/10.7554/eLife.82301