Using unsupervised capsule neural network reveal spatial representations in the human brain.

ABSTRACT: Humans can extract high-level spatial features from visual signals, but spatial representations in the brain are complex and remain unclear. The unsupervised capsule neural network (U-CapsNet) is sensitive to the spatial location and relationship of the object, contains a special recurrent mechanism and uses a self-supervised generation strategy to represent images, which is similar to the computational principle in the human brain. Therefore, we hypothesized that U-CapsNet can help us understand how the human brain processes spatial information. First, brain activities were studied using functional magnetic resonance imaging during spatial working memory in which participants had to remember the locations of circles for a short time. Then, U-CapsNet served as a computational model of the

SUBMITTER: Wang G 

PROVIDER: S-EPMC10973701 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature