Functional parcellation of mouse visual cortex using statistical techniques reveals response-dependent clustering of cortical processing areas.

ABSTRACT: The visual cortex of the mouse brain can be divided into ten or more areas that each contain complete or partial retinotopic maps of the contralateral visual field. It is generally assumed that these areas represent discrete processing regions. In contrast to the conventional input-output characterizations of neuronal responses to standard visual stimuli, here we asked whether six of the core visual areas have responses that are functionally distinct from each other for a given visual stimulus set, by applying machine learning techniques to distinguish the areas based on their activity patterns. Visual areas defined by retinotopic mapping were examined using supervised classifiers applied to responses elicited by a range of stimuli. Using two distinct datasets obtained using wide-field and

SUBMITTER: Kumar MG 

PROVIDER: S-EPMC7888605 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature