The structure of correlated variability reflects task-relevant information in sensory neurons
- 1Department of Neurobiology and Neuroscience Institute, The University of Chicago
- 2Grossman Center for Quantitative Biology and Human Behavior, University of Chicago
- 3Department of Statistics, University of Chicago
Abstract
Shared trial-to-trial variability across sensory neurons is reliably reduced when perceptual performance improves, yet this variability is low dimensional, so it could be ignored by an optimal readout mechanism. Why then is it so consistently related to behavior? We propose that shared variability both reflects circuit structure and reveals the information communicated to downstream areas. In this framework, the same connectivity that shapes signal propagation also shapes shared variability. Using a circuit model, we show that when sensory signals align with shared variability, behaviorally relevant information is amplified without compromising coding fidelity. Analyses of neural population recordings from multiple brain areas and tasks reveal that the dominant axis of shared variability consistently aligns with task-relevant stimulus features and action plans. Finally, the behavioral impact of microstimulation can be explained by the extent to which it changes projections onto the shared variability axis. These findings suggest that shared variability may illuminate, rather than obscure, the neural dimensions that guide behavior.
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BibTeX
@article{srinath2026structure,
title = {{The structure of correlated variability reflects task-relevant information in sensory neurons}},
author = {Ramanujan Srinath and Yunlong Xu and Douglas A. Ruff and Amy M. Ni and Brent Doiron and Marlene R. Cohen},
journal = {Proceedings of the National Academy of Sciences},
year = {2026},
month = jul,
doi = {10.1073/pnas.2523217123},
url = {https://doi.org/10.1073/pnas.2523217123},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - The structure of correlated variability reflects task-relevant information in sensory neurons
AU - Ramanujan Srinath
AU - Yunlong Xu
AU - Douglas A. Ruff
AU - Amy M. Ni
AU - Brent Doiron
AU - Marlene R. Cohen
PY - 2026
DA - 2026/07/07
JO - Proceedings of the National Academy of Sciences
DO - 10.1073/pnas.2523217123
UR - https://doi.org/10.1073/pnas.2523217123
AB - Shared trial-to-trial variability across sensory neurons is reliably reduced when perceptual performance improves, yet this variability is low dimensional, so it could be ignored by an optimal readout mechanism. Why then is it so consistently related to behavior? We propose that shared variability both reflects circuit structure and reveals the information communicated to downstream areas. In this framework, the same connectivity that shapes signal propagation also shapes shared variability. Using a circuit model, we show that when sensory signals align with shared variability, behaviorally relevant information is amplified without compromising coding fidelity. Analyses of neural population recordings from multiple brain areas and tasks reveal that the dominant axis of shared variability consistently aligns with task-relevant stimulus features and action plans. Finally, the behavioral impact of microstimulation can be explained by the extent to which it changes projections onto the shared variability axis. These findings suggest that shared variability may illuminate, rather than obscure, the neural dimensions that guide behavior.
SN - 0027-8424
SN - 1091-6490
ER -