Population coding under the scale invariance of high-dimensional noise
4 affiliations
- 1Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- 2School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
- 3Center for Human Nature, Artificial Intelligence, and Neuroscience (CHAIN), Hokkaido University, Sapporo, Japan
- 4Graduate School of Informatics, Kyoto University, Kyoto, Japan
Abstract
High-dimensional scale-invariant neural activity is ubiquitous across brain regions and species, but its implications for information coding remain unclear. Here, we ask how stimulus information in the high-dimensional activity of mouse V1 scales with neuron number: Does it saturate due to noise correlations or increase without bound as subpopulations grow? Contrary to previous reports, we find that leading noise components that scale linearly with population size, and thus can limit information, are not sufficiently aligned with the signal to impose a bound. This conclusion follows from two scale-invariant power-law properties of neuronal responses in mouse V1: the noise eigenspectrum and alignment of noise components with the signal. We show that population subsampling links the observed power-law exponents to information boundedness and that information scaling depends on the full eigenspectrum rather than its leading modes. Last, we prove that, under subsampling, information-limiting correlations, if present, are differential correlations. Our findings clarify how information scales in high-dimensional neuronal activity under scale-invariant noise.
Related
- Mutual Information in High-Dimensional Neural Coding
- Structural polymorphism and population-variable coding capacity of HERV-K(HML-2) in human pangenomes
- RiboGRAM learns multi-scale non-coding RNA grammar for structural and regulatory inference
- Human single-neuron recordings reveal population coding of attentional dynamics during naturalistic movie viewing
- Comparative evolutionary pharmacogenomics of human prostaglandin-endoperoxide synthase paralogs identifies population-structured coding variation and protein-contextual candidates in N-terminal leader-sequence and catalytic-channel regions
BibTeX
@article{moosavi2026population,
title = {{Population coding under the scale invariance of high-dimensional noise}},
author = {S. Amin Moosavi and Sai Sumedh R. Hindupur and Hideaki Shimazaki},
journal = {Science Advances},
year = {2026},
month = sep,
doi = {10.1126/sciadv.adz9632},
url = {https://doi.org/10.1126/sciadv.adz9632},
note = {CC BY},
}
RIS
TY - JOUR
TI - Population coding under the scale invariance of high-dimensional noise
AU - S. Amin Moosavi
AU - Sai Sumedh R. Hindupur
AU - Hideaki Shimazaki
PY - 2026
DA - 2026/09/25
JO - Science Advances
DO - 10.1126/sciadv.adz9632
UR - https://doi.org/10.1126/sciadv.adz9632
AB - High-dimensional scale-invariant neural activity is ubiquitous across brain regions and species, but its implications for information coding remain unclear. Here, we ask how stimulus information in the high-dimensional activity of mouse V1 scales with neuron number: Does it saturate due to noise correlations or increase without bound as subpopulations grow? Contrary to previous reports, we find that leading noise components that scale linearly with population size, and thus can limit information, are not sufficiently aligned with the signal to impose a bound. This conclusion follows from two scale-invariant power-law properties of neuronal responses in mouse V1: the noise eigenspectrum and alignment of noise components with the signal. We show that population subsampling links the observed power-law exponents to information boundedness and that information scaling depends on the full eigenspectrum rather than its leading modes. Last, we prove that, under subsampling, information-limiting correlations, if present, are differential correlations. Our findings clarify how information scales in high-dimensional neuronal activity under scale-invariant noise.
SN - 2375-2548
ER -