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Recurrent neural network models of multi-area computation underlying decision-making

Michael Kleinman, View ORCID ProfileChandramouli Chandrasekaran, Jonathan C. Kao
doi: https://doi.org/10.1101/798553
Michael Kleinman
aDepartment of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
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Chandramouli Chandrasekaran
bDepartment of Anatomy & Neurobiology, Boston University School of Medicine, Boston, MA, USA
cDepartment of Psychological and Brain Sciences, Boston University, Boston, MA, USA
dCenter for Systems Neuroscience, Boston University, Boston, MA, USA
eDepartment of Biomedical Engineering, Boston University, Boston, MA, USA
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  • ORCID record for Chandramouli Chandrasekaran
  • For correspondence: kao@seas.ucla.edu cchandr1@bu.edu
Jonathan C. Kao
aDepartment of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
fNeurosciences Program, University of California, Los Angeles, CA, USA
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  • For correspondence: kao@seas.ucla.edu cchandr1@bu.edu
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Abstract

Cognition emerges from coordinated computations across multiple brain areas. However, elucidating these computations within and across brain regions is challenging because intra- and inter-area connectivity are typically unknown. To study coordinated computation, we trained multi-area recurrent neural networks (RNNs) to discriminate the dominant color of a checker-board and output decision variables reflecting a direction decision, a task previously used to investigate decision-related dynamics in dorsal premotor cortex (PMd) of monkeys. We found that multi-area RNNs, trained with neurophysiological connectivity constraints and Dale’s law, recapitulated decision-related dynamics observed in PMd. The RNN solved this task by a dynamical mechanism where the direction decision was computed and outputted, via precisely oriented dynamics, on an axis that was nearly orthogonal to checkerboard color inputs. This orthogonal direction information was preferentially propagated through alignment with inter-area connections; in contrast, color information was filtered. These results suggest that cortex uses modular computation to generate minimal sufficient representations of task information. Finally, we used multi-area RNNs to produce experimentally testable hypotheses for computations that occur within and across multiple brain areas, enabling new insights into distributed computation in neural systems.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Email addresses: michael.kleinman{at}ucla.edu (Michael Kleinman), cchandr1{at}bu.edu (Chandramouli Chandrasekaran), kao{at}seas.ucla.edu (Jonathan C. Kao)

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted November 06, 2020.
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Recurrent neural network models of multi-area computation underlying decision-making
Michael Kleinman, Chandramouli Chandrasekaran, Jonathan C. Kao
bioRxiv 798553; doi: https://doi.org/10.1101/798553
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Recurrent neural network models of multi-area computation underlying decision-making
Michael Kleinman, Chandramouli Chandrasekaran, Jonathan C. Kao
bioRxiv 798553; doi: https://doi.org/10.1101/798553

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