Deep Residual Network Predicts Cortical Representation and Organization of Visual Features for Rapid Categorization.

ABSTRACT: The brain represents visual objects with topographic cortical patterns. To address how distributed visual representations enable object categorization, we established predictive encoding models based on a deep residual network, and trained them to predict cortical responses to natural movies. Using this predictive model, we mapped human cortical representations to 64,000 visual objects from 80 categories with high throughput and accuracy. Such representations covered both the ventral and dorsal pathways, reflected multiple levels of object features, and preserved semantic relationships between categories. In the entire visual cortex, object representations were organized into three clusters of categories: biological objects, non-biological objects, and background scenes. In a finer scale s

SUBMITTER: Wen H 

PROVIDER: S-EPMC5830584 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature