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Sequential and efficient neural-population coding of complex task information

View ORCID ProfileSue Ann Koay, View ORCID ProfileAdam S. Charles, View ORCID ProfileStephan Y. Thiberge, View ORCID ProfileCarlos D. Brody, View ORCID ProfileDavid W. Tank
doi: https://doi.org/10.1101/801654
Sue Ann Koay
1Princeton Neuroscience Institute, Princeton University; Princeton NJ 08544; USA
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Adam S. Charles
1Princeton Neuroscience Institute, Princeton University; Princeton NJ 08544; USA
2Present affiliations: Department of Biomedical Engineering; Center for Imaging Science; Kavli Neuroscience Discovery Institute; Mathematical Institute for Data Science; all at Johns Hopkins University, Baltimore MD 21218; USA
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Stephan Y. Thiberge
3Bezos Center for Neural Circuit Dynamics, Princeton University; Princeton NJ 08544; USA
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Carlos D. Brody
1Princeton Neuroscience Institute, Princeton University; Princeton NJ 08544; USA
4Howard Hughes Medical Institute, Princeton University; Princeton NJ 08544; USA
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  • For correspondence: brody@princeton.edu dwtank@princeton.edu
David W. Tank
1Princeton Neuroscience Institute, Princeton University; Princeton NJ 08544; USA
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  • For correspondence: brody@princeton.edu dwtank@princeton.edu
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Summary

Recent work has highlighted that many types of variables are represented in each neocortical area. How can these many neural representations be organized together without interference, and coherently maintained/updated through time? We recorded from large neural populations in posterior cortices as mice performed a complex, dynamic task involving multiple interrelated variables. The neural encoding implied that correlated task variables were represented by uncorrelated neural-population modes, while pairs of neurons exhibited a variety of signal correlations. This finding relates to principles of efficient coding for task-specific information, with neural-population modes as the encoding unit, and applied across posterior cortical regions and layers 2/3 and 5. Remarkably, this encoding function was multiplexed with sequential neural dynamics as well as reliably followed changes in task-variable correlations through time. We suggest that neural circuits can implement time-dependent encoding in a simple way by using random sequential dynamics as a temporal scaffold.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Revised methods and a stricter null hypothesis test for encoding geometry analysis. Expanded theoretical exploration of properties of multiplicative neural sequences. Removed decoding geometry analyses.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted January 18, 2021.
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Sequential and efficient neural-population coding of complex task information
Sue Ann Koay, Adam S. Charles, Stephan Y. Thiberge, Carlos D. Brody, David W. Tank
bioRxiv 801654; doi: https://doi.org/10.1101/801654
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Sequential and efficient neural-population coding of complex task information
Sue Ann Koay, Adam S. Charles, Stephan Y. Thiberge, Carlos D. Brody, David W. Tank
bioRxiv 801654; doi: https://doi.org/10.1101/801654

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