A recurrent neural model for proto-object based contour integration and figure-ground segregation.

ABSTRACT: Visual processing of objects makes use of both feedforward and feedback streams of information. However, the nature of feedback signals is largely unknown, as is the identity of the neuronal populations in lower visual areas that receive them. Here, we develop a recurrent neural model to address these questions in the context of contour integration and figure-ground segregation. A key feature of our model is the use of grouping neurons whose activity represents tentative objects ("proto-objects") based on the integration of local feature information. Grouping neurons receive input from an organized set of local feature neurons, and project modulatory feedback to those same neurons. Additionally, inhibition at both the local feature level and the object representation level biases the inter

SUBMITTER: Hu B 

PROVIDER: S-EPMC5693639 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature