Recurrent connectivity supports higher-level visual and semantic object representations in the brain.

ABSTRACT: Visual object recognition has been traditionally conceptualised as a predominantly feedforward process through the ventral visual pathway. While feedforward artificial neural networks (ANNs) can achieve human-level classification on some image-labelling tasks, it's unclear whether computational models of vision alone can accurately capture the evolving spatiotemporal neural dynamics. Here, we probe these dynamics using a combination of representational similarity and connectivity analyses of fMRI and MEG data recorded during the recognition of familiar, unambiguous objects. Modelling the visual and semantic properties of our stimuli using an artificial neural network as well as a semantic feature model, we find that unique aspects of the neural architecture and connectivity dynamics relate

SUBMITTER: von Seth J 

PROVIDER: S-EPMC10682037 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature