The geometry of representational drift in natural and artificial neural networks.

ABSTRACT: Neurons in sensory areas encode/represent stimuli. Surprisingly, recent studies have suggested that, even during persistent performance, these representations are not stable and change over the course of days and weeks. We examine stimulus representations from fluorescence recordings across hundreds of neurons in the visual cortex using in vivo two-photon calcium imaging and we corroborate previous studies finding that such representations change as experimental trials are repeated across days. This phenomenon has been termed "representational drift". In this study we geometrically characterize the properties of representational drift in the primary visual cortex of mice in two open datasets from the Allen Institute and propose a potential mechanism behind such drift. We observe representa

SUBMITTER: Aitken K 

PROVIDER: S-EPMC9731438 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature