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Mutual Information in High-Dimensional Neural Coding

Year: 2026

Type: preprint

Abstract: Estimating mutual information in large neural populations is limited by the exponential growth of possible neural response patterns. Here we apply a Monte Carlo framework to make this problem tractable by sampling from a conditionally independent population model fit from single-neuron responses. The estimator accurately recovers analytical ground truth, corrects finite-data bias, and scales to empirical recordings containing 100 to 10,000 neurons from visual cortex, hippocampus, and retina. Across systems, stimulus uncertainty decays exponentially with population size, revealing a simple scaling law for high-dimensional neural codes.

Source: PubMed

DOI: 10.48550/arxiv.2610.22206

Authors: Li, Zimo, Tian, Yuchang, Leighton, Matthew P., Lynn, Christopher W.

Institution: Yale University

Language: English

Topic: Neural dynamics and brain function

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: green

Funders: Yale University, National Institutes of Health, Raymond and Beverly Sackler Institute for Biological, Physical and Engineering Sciences, Yale University, Natural Sciences and Engineering Research Council of Canada, National Institute of General Medical Sciences

Award: Minimax entropy: Optimal statistical models for neural activity and chromatin structure

OpenAlex ID: W7222520469

Cites: 7

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Related to: 10