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Relating natural image statistics to patterns of response covariability in macaque primary visual cortex
Amirhossein Farzmahdi1,2, Adam Kohn3,4,5, Ruben Coen-Cagli6,7,8
1Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, USA. af3587@columbia.edu.
Nature Communications
|July 22, 2025
Summary
Brain activity variability, or covariability, reflects uncertainty in processing visual information. This study links neural activity patterns to natural image statistics, revealing how the brain represents scenes.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Computer Vision
Background:
- Understanding neural coding requires analyzing cortical activity structure and shared variability.
- The role of neural covariability in representing natural visual input is not well understood.
Purpose of the Study:
- To investigate the relationship between neural covariability and natural image statistics.
- To extend generative models of image statistics to explain pairwise neural activity.
Main Methods:
- Adopted the neural sampling hypothesis and extended a generative model of image statistics.
- Modeled pairwise neural activity as joint probabilistic inferences about latent image features.
- Recorded neural activity from macaque primary visual cortex (V1).
Main Results:
- Variability in neural activity reflects uncertainty about latent image features.
- Spatial context in images influences shared versus independent uncertainty.
- Model predicted that image size affects neural correlations based on receptive field overlap.
- Predictions were confirmed by V1 recordings.
Conclusions:
- Established a precise connection between V1 correlations and natural scene statistics.
- Suggests neural covariability patterns are integral to probabilistic scene representations.
