Higher-level spatial prediction in natural vision across mouse visual cortex.

ABSTRACT: Theories of predictive processing propose that sensory systems constantly predict incoming signals, based on spatial and temporal context. However, evidence for prediction in sensory cortex largely comes from artificial experiments using simple, highly predictable stimuli, that arguably encourage prediction. Here, we test for sensory prediction during natural scene perception. Specifically, we use deep generative modelling to quantify the spatial predictability of receptive field (RF) patches in natural images, and compared those predictability estimates to brain responses in the mouse visual cortex-while rigorously accounting for established tuning to a rich set of low-level image features and their local statistical context-in a large scale survey of high-density recordings from the Alle

SUBMITTER: Heilbron M 

PROVIDER: S-EPMC12829946 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature