Recurrent issues with deep neural network models of visual recognition.

ABSTRACT: Object recognition requires flexible and robust information processing, especially in view of the challenges posed by naturalistic visual settings. The ventral stream in visual cortex is provided with this robustness by its recurrent connectivity. Recurrent deep neural networks (DNNs) have recently emerged as promising models of the ventral stream, surpassing feedforward DNNs in the ability to account for brain representations. In this study, we asked whether recurrent DNNs could also better account for human behaviour during visual recognition. We assembled a stimulus set that includes manipulations that are often associated with recurrent processing in the literature, like occlusion, partial viewing, clutter, and spatial phase scrambling. We obtained a benchmark dataset from human partic

SUBMITTER: Maniquet T 

PROVIDER: S-EPMC12534488 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature