Modelling the neural code in large populations of correlated neurons

  1. Sacha Sokoloski  Is a corresponding author
  2. Amir Aschner
  3. Ruben Coen-Cagli  Is a corresponding author
  1. Albert Einstein College of Medicine, United States

Abstract

Neurons respond selectively to stimuli, and thereby define a code that associates stimuli with population response patterns. Certain correlations within population responses (noise correlations) significantly impact the information content of the code, especially in large populations. Understanding the neural code thus necessitates response models that quantify the coding properties of modelled populations, while fitting large-scale neural recordings and capturing noise correlations. In this paper we propose a class of response model based on mixture models and exponential families. We show how to fit our models with expectation-maximization, and that they capture diverse variability and covariability in recordings of macaque primary visual cortex. We also show how they facilitate accurate Bayesian decoding, provide a closed-form expression for the Fisher information, and are compatible with theories of probabilistic population coding. Our framework could allow researchers to quantitatively validate the predictions of neural coding theories against both large-scale neural recordings and cognitive performance.

Data availability

All data used in this study is available at the Git repository (https://gitlab.com/sacha-sokoloski/neural-mixtures). This includes experimental data for model validation, as well as source data for all figures, and code for running simulations.

Article and author information

Author details

  1. Sacha Sokoloski

    Systems and Computational Biology, Albert Einstein College of Medicine, New York City, United States
    For correspondence
    sacha.sokoloski@mailbox.org
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4166-1772
  2. Amir Aschner

    Neuroscience, Albert Einstein College of Medicine, New York City, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Ruben Coen-Cagli

    Neuroscience, Albert Einstein College of Medicine, New York City, United States
    For correspondence
    ruben.coen-cagli@einsteinmed.org
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2052-5894

Funding

National Institutes of Health (EY030578)

  • Ruben Coen-Cagli

National Institutes of Health (EY02826)

  • Sacha Sokoloski
  • Amir Aschner

National Institutes of Health (EY016774)

  • Amir Aschner

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

Reviewing Editor

  1. Jonathan W Pillow, Princeton University, United States

Ethics

Animal experimentation: All procedures were approved by the Institutional Animal Care and Use Committee of the Albert Einstein College of Medicine, and were in compliance with the guidelines set forth in the National Institutes of Health Guide for the Care and Use of Laboratory Animals under protocols 20180308 and 20180309 for the awake and anaesthetized macaque recordings, respectively.

Version history

  1. Received: November 5, 2020
  2. Accepted: October 1, 2021
  3. Accepted Manuscript published: October 5, 2021 (version 1)
  4. Version of Record published: November 9, 2021 (version 2)
  5. Version of Record updated: November 12, 2021 (version 3)

Copyright

© 2021, Sokoloski 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. Sacha Sokoloski
  2. Amir Aschner
  3. Ruben Coen-Cagli
(2021)
Modelling the neural code in large populations of correlated neurons
eLife 10:e64615.
https://doi.org/10.7554/eLife.64615

Share this article

https://doi.org/10.7554/eLife.64615

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