Recurrent processing improves occluded object recognition and gives rise to perceptual hysteresis.

ABSTRACT: Over the past decades, object recognition has been predominantly studied and modelled as a feedforward process. This notion was supported by the fast response times in psychophysical and neurophysiological experiments and the recent success of deep feedforward neural networks for object recognition. Recently, however, this prevalent view has shifted and recurrent connectivity in the brain is now believed to contribute significantly to object recognition - especially under challenging conditions, including the recognition of partially occluded objects. Moreover, recurrent dynamics might be the key to understanding perceptual phenomena such as perceptual hysteresis. In this work we investigate if and how artificial neural networks can benefit from recurrent connections. We systematically com

SUBMITTER: Ernst MR 

PROVIDER: S-EPMC8684313 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature