Boundary assignment in a recurrent network architecture.

We describe a model and simulations of boundary assignment by cortical neurons, a process that assigns edges to figural image regions, as opposed to the background regions on the other side of the edge. The model is composed of several areas, resembling the hierarchical feedforward-feedback organization of areas in the visual cortex. In each successive area along the hierarchy, the visual image is represented at a coarser resolution. Model neurons tend to assign edges to convex image regions. Because of high spatial resolution, information about convexity is not immediately available to all neurons in lower-level areas. In higher-level areas, however, spatial resolution is low, and convexity is coded more reliably. Feedback connections propagate this information to the high-resolution neurons of lower-level visual areas, making it available at all network levels and at all spatial resolutions. The proposed connection scheme assigns edges faster and more reliable to objects than one with only horizontal connections. The model accounts for both psychophysical and neurophysiological data on figural assignment.