NN for proto-object based contour integration and figure-ground segregation (Hu & Niebur 2017)

"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. ..."

Model Type: Connectionist Network

Region(s) or Organism(s): Neocortex

Cell Type(s): Abstract rate-based neuron

Model Concept(s): Vision

Simulation Environment: MATLAB (web link to model)

Implementer(s): Hu, Brian [bhu6 (AT) jhmi (DOT) edu]; Jeck, Danny


Hu B, Niebur E. (2017). A recurrent neural model for proto-object based contour integration and figure-ground segregation. Journal of computational neuroscience. 43 [PubMed]

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