Motor-related signals support localization invariance for stable visual perception.

ABSTRACT: Our ability to perceive a stable visual world in the presence of continuous movements of the body, head, and eyes has puzzled researchers in the neuroscience field for a long time. We reformulated this problem in the context of hierarchical convolutional neural networks (CNNs)-whose architectures have been inspired by the hierarchical signal processing of the mammalian visual system-and examined perceptual stability as an optimization process that identifies image-defining features for accurate image classification in the presence of movements. Movement signals, multiplexed with visual inputs along overlapping convolutional layers, aided classification invariance of shifted images by making the classification faster to learn and more robust relative to input noise. Classification invarianc

SUBMITTER: Benucci A 

PROVIDER: S-EPMC8947590 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature