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arXiv · 2610.22206

Mutual Information in High-Dimensional Neural Coding

Zimo Li·Yuchang Tian·Matthew P. Leighton·Christopher W. Lynn
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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.

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Zimo Li, Yuchang Tian, Matthew P. Leighton, Christopher W. Lynn. 2026-10-15. Mutual Information in High-Dimensional Neural Coding. https://arxiv.org/abs/2610.22206

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