Scaling of sensory information in large neural populations shows signatures of information-limiting correlations

MohammadMehdi Kafashan, Anna W. Jaffe, Selmaan N. Chettih, Ramon Nogueira, Iñigo Arandia-Romero, Christopher D. Harvey, Rubén Moreno-Bote and Jan Drugowitsch ()
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MohammadMehdi Kafashan: Harvard Medical School
Anna W. Jaffe: Harvard Medical School
Selmaan N. Chettih: Harvard Medical School
Ramon Nogueira: Columbia University
Iñigo Arandia-Romero: University of Zaragoza
Christopher D. Harvey: Harvard Medical School
Rubén Moreno-Bote: Universitat Pompeu Fabra
Jan Drugowitsch: Harvard Medical School

Nature Communications, 2021, vol. 12, issue 1, 1-16

Abstract: 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.

Date: 2021
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DOI: 10.1038/s41467-020-20722-y

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