Predictive coding of natural images by V1 firing rates and rhythmic synchronization.

ABSTRACT: Predictive coding is an important candidate theory of self-supervised learning in the brain. Its central idea is that sensory responses result from comparisons between bottom-up inputs and contextual predictions, a process in which rates and synchronization may play distinct roles. We recorded from awake macaque V1 and developed a technique to quantify stimulus predictability for natural images based on self-supervised, generative neural networks. We find that neuronal firing rates were mainly modulated by the contextual predictability of higher-order image features, which correlated strongly with human perceptual similarity judgments. By contrast, V1 gamma (γ)-synchronization increased monotonically with the contextual predictability of low-level image features and emerged exclusively for

SUBMITTER: Uran C 

PROVIDER: S-EPMC8992798 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature