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- W3200920912 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." @default.
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- W3200920912 date "2021-09-20" @default.
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- W3200920912 title "Distributed context-dependent choice information in mouse posterior cortex" @default.
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- W3200920912 doi "https://doi.org/10.21203/rs.3.rs-907578/v1" @default.
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