Modelling the neural code in large populations of correlated neurons
- 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
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.
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.
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.
Metrics
-
- 2,097
- views
-
- 325
- downloads
-
- 10
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
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)
Modelling the neural code in large populations of correlated neurons
eLife 10:e64615.
https://doi.org/10.7554/eLife.64615
