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.
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.
- Ruben Coen-Cagli
- Sacha Sokoloski
- Amir Aschner
- Amir Aschner
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
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.
- Jonathan W Pillow, Princeton University, United States
- Received: November 5, 2020
- Accepted: October 1, 2021
- Accepted Manuscript published: October 5, 2021 (version 1)
© 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.