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Distributed context-dependent choice information in mouse posterior cortex

Javier G. Orlandi, Mohammad Abdolrahmani, Ryo Aoki, Dmitry R. Lyamzin and Andrea Benucci (andrea.benucci@riken.jp)
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Javier G. Orlandi: RIKEN Center for Brain Science
Mohammad Abdolrahmani: RIKEN Center for Brain Science
Ryo Aoki: RIKEN Center for Brain Science
Dmitry R. Lyamzin: RIKEN Center for Brain Science
Andrea Benucci: RIKEN Center for Brain Science

Nature Communications, 2023, vol. 14, issue 1, 1-16

Abstract: Abstract Choice information appears in multi-area brain networks mixed with sensory, motor, and cognitive variables. In the posterior cortex—traditionally implicated in decision computations—the presence, strength, and area specificity of choice signals are highly variable, limiting a cohesive understanding of their computational significance. Examining the mesoscale activity in the mouse posterior cortex during a visual task, we found that choice signals defined a decision variable in a low-dimensional embedding space with a prominent contribution along the ventral visual stream. Their subspace was near-orthogonal to concurrently represented sensory and motor-related activations, with modulations by task difficulty and by the animals’ attention state. A recurrent neural network trained with animals’ choices revealed an equivalent decision variable whose context-dependent dynamics agreed with that of the neural data. Our results demonstrated an independent, multi-area decision variable in the posterior cortex, controlled by task features and cognitive demands, possibly linked to contextual inference computations in dynamic animal–environment interactions.

Date: 2023
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DOI: 10.1038/s41467-023-35824-6

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