Scaling of sensory information in large neural populations shows signatures of information-limiting correlations
Open Access
- 20 January 2021
- journal article
- research article
- Published by Springer Science and Business Media LLC in Nature Communications
- Vol. 12 (1), 1-16
- https://doi.org/10.1038/s41467-020-20722-y
Abstract
How is information distributed across large neuronal populations within a given brain area? Information may be distributed roughly evenly across neuronal populations, so that total information scales linearly with the number of recorded neurons. Alternatively, the neural code might be highly redundant, meaning that total information saturates. Here we investigate how sensory information about the direction of a moving visual stimulus is distributed across hundreds of simultaneously recorded neurons in mouse primary visual cortex. We show that information scales sublinearly due to correlated noise in these populations. We compartmentalized noise correlations into information-limiting and nonlimiting components, then extrapolate to predict how information grows with even larger neural populations. We predict that tens of thousands of neurons encode 95% of the information about visual stimulus direction, much less than the number of neurons in primary visual cortex. These findings suggest that the brain uses a widely distributed, but nonetheless redundant code that supports recovering most sensory information from smaller subpopulations. Information regarding a sensory stimulus is distributed in activity of neuronal populations. Here the authors show stimulus information scales sub-linearly with the number of neurons in mouse visual cortex due to correlated noise and may saturate in far fewer numbers of neurons than the total in V1.Funding Information
- Gatsby Charitable Foundation
- National Science Foundation (DBI-1707398)
- U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (R01MH107620, R01MH115554)
- U.S. Department of Health & Human Services | National Institutes of Health (R01NS089521, R01NS108410)
- U.S. Department of Health & Human Services | National Institutes of Health
- James S. McDonnell Foundation (220020462)
- U.S. Department of Health & Human Services | NIH | National Institute of Mental Health
This publication has 89 references indexed in Scilit:
- Locomotion Controls Spatial Integration in Mouse Visual CortexCurrent Biology, 2013
- Choice-specific sequences in parietal cortex during a virtual-navigation decision taskNature, 2012
- Perceptual Learning Reduces Interneuronal Correlations in Macaque Visual CortexNeuron, 2011
- Bayesian sampling in visual perceptionProceedings of the National Academy of Sciences of the United States of America, 2011
- Measuring and interpreting neuronal correlationsNature Neuroscience, 2011
- Statistically optimal perception and learning: from behavior to neural representationsTrends in Cognitive Sciences, 2010
- Noise in the nervous systemNature Reviews Neuroscience, 2008
- PsychoPy—Psychophysics software in PythonJournal of Neuroscience Methods, 2007
- Optimal decoding of correlated neural population responses in the primate visual cortexNature Neuroscience, 2006
- Amplification of Trial-to-Trial Response Variability by Neurons in Visual CortexPLoS Biology, 2004