Associations betweenin vitro,in vivoandin silicocell classes in mouse primary visual cortex

Abstract

AbstractThe brain consists of many cell classes yetin vivoelectrophysiology recordings are typically unable to identify and monitor their activity in the behaving animal. Here, we employed a systematic approach to link cellular, multi-modalin vitroproperties from experiments within vivorecorded units via computational modeling and optotagging experiments. We found two one-channel and six multi-channel clusters in mouse visual cortex with distinctin vivoproperties in terms of activity, cortical depth, and behavior. We used biophysical models to map the two one- and the six multi-channel clusters to specificin vitroclasses with unique morphology, excitability and conductance properties that explain their distinct extracellular signatures and functional characteristics. These concepts were tested in ground-truth optotagging experiments with two inhibitory classes unveiling distinctin vivoproperties. This multi-modal approach presents a powerful way to separatein vivoclusters and infer their cellular properties from first principles.