1. Neuroscience

Emergence of time persistence in a data-driven neural network model

  1. Sebastien Wolf
  2. Guillaume Le Goc
  3. Georges Debrégeas
  4. Simona Cocco
  5. Rémi Monasson  Is a corresponding author
  1. Ecole Normale Superieure, CNRS UMR 8023, France
  2. Sorbonne Université, CNRS, France
https://doi.org/10.7554/eLife.79541
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  1. Sebastien Wolf
  2. Guillaume Le Goc
  3. Georges Debrégeas
  4. Simona Cocco
  5. Rémi Monasson
(2023)
Emergence of time persistence in a data-driven neural network model
eLife 12:e79541.
https://doi.org/10.7554/eLife.79541
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Abstract

Establishing accurate as well as interpretable models of network activity is an open challenge in systems neuroscience. Here we infer an energy-based model of the ARTR, a circuit that controls zebrafish swimming statistics, using functional recordings of the spontaneous activity of hundreds of neurons. Although our model is trained to reproduce the low-order statistics of the network activity at short time-scales, its simulated dynamics quantitatively captures the slowly alternating activity of the ARTR. It further reproduces the modulation of this persistent dynamics by the water temperature and visual stimulation. Mathematical analysis of the model unveils a low-dimensional landscape-based representation of the ARTR activity, where the slow network dynamics reflects Arrhenius-like barriers crossings between metastable states. Our work thus shows how data-driven models built from large neural populations recordings can be reduced to low-dimensional functional models in order to reveal the fundamental mechanisms controlling the collective neuronal dynamics.

Data availability

All data and new codes necessary to reproduce the results reported in this work can be accessed from the dataset URL below.

The following data sets were generated

Article and author information

Author details

  1. Sebastien Wolf

    Laboratoire de Physique de l'Ecole Normale Supérieure, Ecole Normale Superieure, CNRS UMR 8023, Paris, France
    Competing interests
    The authors declare that no competing interests exist.

  2. Guillaume Le Goc

    Laboratoire Jean Perrin, Sorbonne Université, CNRS, Paris, France
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6946-1142

  3. Georges Debrégeas

    Laboratoire Jean Perrin, Sorbonne Université, CNRS, Paris, France
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3698-4497

  4. Simona Cocco

    Laboratoire de Physique de l'Ecole Normale Supérieure, Ecole Normale Superieure, CNRS UMR 8023, Paris, France
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1852-7789

  5. Rémi Monasson

    Laboratoire de Physique de l'Ecole Normale Supérieure, Ecole Normale Superieure, CNRS UMR 8023, Paris, France
    For correspondence
    remi.monasson@phys.ens.fr
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4459-0204

Funding

Agence Nationale de la Recherche (Locomat)

  • Rémi Monasson

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

Reviewing Editor

  1. Tatyana O Sharpee, Salk Institute for Biological Studies, United States

Ethics

Animal experimentation: All experiments were approved by Le Comité d'Éthique pour l'Expérimentation Animale Charles Darwin (02601.01)

Version history

  1. Preprint posted: February 4, 2022 (view preprint)
  2. Received: April 17, 2022
  3. Accepted: March 13, 2023
  4. Accepted Manuscript published: March 14, 2023 (version 1)
  5. Version of Record published: May 10, 2023 (version 2)

Copyright

© 2023, Wolf 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. Sebastien Wolf
  2. Guillaume Le Goc
  3. Georges Debrégeas
  4. Simona Cocco
  5. Rémi Monasson
(2023)
Emergence of time persistence in a data-driven neural network model
eLife 12:e79541.
https://doi.org/10.7554/eLife.79541

Share this article

https://doi.org/10.7554/eLife.79541

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