PreprintarXiv licencearXiv
Mutual Information in High-Dimensional Neural Coding
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.
Related
- Hybrid spatial organization and evidence for magnitude-independent neural coding of linguistic information during sentence production
- Selection vs. integration task demands shape the similarity of information neural coding
- Population coding under the scale invariance of high-dimensional noise
- An information-borrowing Bayesian method for inferring high-dimensional population-level brain connectivity from small samples
- High-Dimensional Information Transfer from the Spinal Cord to the Brain using Spinal Cord Stimulation
BibTeX
@misc{li2026mutual,
title = {{Mutual Information in High-Dimensional Neural Coding}},
author = {Zimo Li and Yuchang Tian and Matthew P. Leighton and Christopher W. Lynn},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.22206},
eprint = {2610.22206},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.22206},
}
RIS
TY - UNPB
TI - Mutual Information in High-Dimensional Neural Coding
AU - Zimo Li
AU - Yuchang Tian
AU - Matthew P. Leighton
AU - Christopher W. Lynn
PY - 2026
DA - 2026/10/15
PB - arXiv
DO - 10.48550/arxiv.2610.22206
UR - https://arxiv.org/abs/2610.22206
AB - 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.
ER -