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

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.

Ethics

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

Reviewing Editor

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

Publication history

  1. Received: April 17, 2022
  2. Accepted: March 13, 2023
  3. Accepted Manuscript published: March 14, 2023 (version 1)

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.

Metrics

  • 157
    Page views
  • 39
    Downloads
  • 0
    Citations

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

Further reading

    1. Neuroscience
    Tarek Amer, Lila Davachi
    Review Article

    Pattern separation, or the process by which highly similar stimuli or experiences in memory are represented by non-overlapping neural ensembles, has typically been ascribed to processes supported by the hippocampus. Converging evidence from a wide range of studies, however, suggests that pattern separation is a multistage process supported by a network of brain regions. Based on this evidence, considered together with related findings from the interference resolution literature, we propose the ‘cortico-hippocampal pattern separation’ (CHiPS) framework, which asserts that brain regions involved in cognitive control play a significant role in pattern separation. Particularly, these regions may contribute to pattern separation by (1) resolving interference in sensory regions that project to the hippocampus, thus regulating its cortical input, or (2) directly modulating hippocampal processes in accordance with task demands. Considering recent interest in how hippocampal operations are modulated by goal states likely represented and regulated by extra-hippocampal regions, we argue that pattern separation is similarly supported by neocortical–hippocampal interactions.

    1. Medicine
    2. Neuroscience
    Christian Büchel
    Review Article

    Chronic, or persistent pain affects more than 10% of adults in the general population. This makes it one of the major physical and mental health care problems. Although pain is an important acute warning signal that allows the organism to take action before tissue damage occurs, it can become persistent and its role as a warning signal thereby inadequate. Although per definition, pain can only be labeled as persistent after 3 months, the trajectory from acute to persistent pain is likely to be determined very early and might even start at the time of injury. The biopsychosocial model has revolutionized our understanding of chronic pain and paved the way for psychological treatments for persistent pain, which routinely outperform other forms of treatment. This suggests that psychological processes could also be important in shaping the very early trajectory from acute to persistent pain and that targeting these processes could prevent the development of persistent pain. In this review, we develop an integrative model and suggest novel interventions during early pain trajectories, based on predictions from this model.