Emergence of time persistence in a data-driven neural network model
- Ecole Normale Superieure, CNRS UMR 8023, France
- 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.
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All data and new codes necessary to reproduce the results reported in this workData Cloud of the Department of Physics of the Ecole Normale Supérieure, Paris.
Article and author information
Author details
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
- Tatyana O Sharpee, Salk Institute for Biological Studies, United States
Publication history
- Received: April 17, 2022
- Accepted: March 13, 2023
- 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.
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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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