The information bottleneck as a principle underlying multi-area cortical representations during decision-making.

ABSTRACT: Decision-making emerges from distributed computations across multiple brain areas, but it is unclear why the brain distributes the computation. In deep learning, artificial neural networks use multiple areas (or layers) and form optimal representations of task inputs. These optimal representations are sufficient to perform the task well, but minimal so they are invariant to other irrelevant variables. We recorded single neurons and multiunits in dorsolateral prefrontal cortex (DLPFC) and dorsal premotor cortex (PMd) in monkeys during a perceptual decision-making task. We found that while DLPFC represents task-related inputs required to compute the choice, the downstream PMd contains a minimal sufficient, or optimal, representation of the choice. To identify a mechanism

SUBMITTER: Kleinman M 

PROVIDER: S-EPMC10369960 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature