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Correlations enhance the behavioral readout of neural population activity in association cortex

Martina Valente, Giuseppe Pica, Caroline A. Runyan, Ari S. Morcos, Christopher D. Harvey, View ORCID ProfileStefano Panzeri
doi: https://doi.org/10.1101/2020.04.03.024133
Martina Valente
1Neural Computation Laboratory, Istituto Italiano di Tecnologia, Rovereto, Italy
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Giuseppe Pica
1Neural Computation Laboratory, Istituto Italiano di Tecnologia, Rovereto, Italy
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Caroline A. Runyan
2Department of Neurobiology, Harvard Medical School, Boston, MA, USA
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Ari S. Morcos
2Department of Neurobiology, Harvard Medical School, Boston, MA, USA
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Christopher D. Harvey
2Department of Neurobiology, Harvard Medical School, Boston, MA, USA
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  • For correspondence: harvey@hms.harvard.edu stefano.panzeri@iit.it
Stefano Panzeri
1Neural Computation Laboratory, Istituto Italiano di Tecnologia, Rovereto, Italy
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  • ORCID record for Stefano Panzeri
  • For correspondence: harvey@hms.harvard.edu stefano.panzeri@iit.it
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Abstract

The spatiotemporal structure of activity in populations of neurons is critical for accurate perception and behavior. Experimental and theoretical studies have focused on “noise” correlations – trial-to-trial covariations in neural activity for a given stimulus – as a key feature of population activity structure. Much work has shown that these correlations limit the stimulus information encoded by a population of neurons, leading to the widely-held prediction that correlations are detrimental for perceptual discrimination behaviors. However, this prediction relies on an untested assumption: that the neural mechanisms that read out sensory information to inform behavior depend only on a population’s total stimulus information independently of how correlations constrain this information across neurons or time. Here we make the critical advance of simultaneously studying how correlations affect both the encoding and the readout of sensory information. We analyzed calcium imaging data from mouse posterior parietal cortex during two perceptual discrimination tasks. Correlations limited the ability to encode stimulus information, but (seemingly paradoxically) correlations were higher when mice made correct choices than when they made errors. On a single-trial basis, a mouse’s behavioral choice depended not only on the stimulus information in the activity of the population as a whole, but unexpectedly also on the consistency of information across neurons and time. Because correlations increased information consistency, sensory information was more efficiently converted into a behavioral choice in the presence of correlations. Given this enhanced-by-consistency readout, we estimated that correlations produced a behavioral benefit that compensated or overcame their detrimental information-limiting effects. These results call for a re-evaluation of the role of correlated neural activity, and suggest that correlations in association cortex can benefit task performance even if they decrease sensory information.

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Posted April 05, 2020.
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Correlations enhance the behavioral readout of neural population activity in association cortex
Martina Valente, Giuseppe Pica, Caroline A. Runyan, Ari S. Morcos, Christopher D. Harvey, Stefano Panzeri
bioRxiv 2020.04.03.024133; doi: https://doi.org/10.1101/2020.04.03.024133
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Correlations enhance the behavioral readout of neural population activity in association cortex
Martina Valente, Giuseppe Pica, Caroline A. Runyan, Ari S. Morcos, Christopher D. Harvey, Stefano Panzeri
bioRxiv 2020.04.03.024133; doi: https://doi.org/10.1101/2020.04.03.024133

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