The feature landscape of visual cortex

Abstract

Understanding computations in the visual system requires a characterization of the distinct feature preferences of neurons in different visual cortical areas. However, we know little about how feature preferences of neurons within a given area relate to that areas role within the global organization of visual cortex. To address this, we recorded from thousands of neurons across six visual cortical areas in mouse and leveraged generative AI methods combined with closed-loop neuronal recordings to identify each neurons visual feature preference. First, we discovered that the mouses visual system is globally organized to encode features in a manner invariant to the types of image transformations induced by self-motion. Second, we found differences in the visual feature preferences of each area and that these differences generalized across animals. Finally, we observed that a given areas collection of preferred stimuli ( own-stimuli) drive neurons from the same area more effectively through their dynamic range compared to preferred stimuli from other areas ( other-stimuli). As a result, feature preferences of neurons within an area are organized to maximally encode differences among own-stimuli while remaining insensitive to differences among other-stimuli. These results reveal how visual areas work together to efficiently encode information about the external world.