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Distributed context-dependent choice information in mouse dorsal-parietal cortex

Javier G. Orlandi, Mohammad Abdolrahmani, Ryo Aoki, Dmitry R. Lyamzin, View ORCID ProfileAndrea Benucci
doi: https://doi.org/10.1101/2021.03.02.433657
Javier G. Orlandi
1RIKEN Center for Brain Science, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan
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Mohammad Abdolrahmani
1RIKEN Center for Brain Science, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan
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Ryo Aoki
1RIKEN Center for Brain Science, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan
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Dmitry R. Lyamzin
1RIKEN Center for Brain Science, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan
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Andrea Benucci
1RIKEN Center for Brain Science, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan
2University of Tokyo, Graduate School of Information Science and Technology, Department of Mathematical Informatics, 1-1-1 Yayoi, Bunkyo City, Tokyo 113-0032, Japan
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  • ORCID record for Andrea Benucci
  • For correspondence: andrea.benucci@riken.jp
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Abstract

Choice information appears in the brain as distributed signals with top-down and bottom-up components that together support decision-making computations. In sensory and associative cortical regions, the presence of choice signals, their strength, and area specificity are known to be elusive and changeable, limiting a cohesive understanding of their computational significance. In this study, examining the mesoscale activity in mouse posterior cortex during a complex visual discrimination task, we found that broadly distributed choice signals defined a decision variable in a low-dimensional embedding space of multi-area activations, particularly along the ventral visual stream. The subspace they defined was near-orthogonal to concurrently represented sensory and motor-related activations, and it was modulated by task difficulty and contextually by the animals’ attention state. To mechanistically relate choice representations to decision-making computations, we trained recurrent neural networks with the animals’ choices and found an equivalent decision variable whose context-dependent dynamics agreed with that of the neural data. In conclusion, our results demonstrated an independent decision variable broadly represented in the posterior cortex, controlled by task features and cognitive demands. Its dynamics reflected decision computations, possibly linked to context-dependent feedback signals used for probabilistic-inference computations in variable animal-environment interactions.

Competing Interest Statement

The authors have declared no competing interest.

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 March 03, 2021.
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Distributed context-dependent choice information in mouse dorsal-parietal cortex
Javier G. Orlandi, Mohammad Abdolrahmani, Ryo Aoki, Dmitry R. Lyamzin, Andrea Benucci
bioRxiv 2021.03.02.433657; doi: https://doi.org/10.1101/2021.03.02.433657
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Distributed context-dependent choice information in mouse dorsal-parietal cortex
Javier G. Orlandi, Mohammad Abdolrahmani, Ryo Aoki, Dmitry R. Lyamzin, Andrea Benucci
bioRxiv 2021.03.02.433657; doi: https://doi.org/10.1101/2021.03.02.433657

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