Implementing the boundary contour system on a multi vector processor iPSC/2 HyperCube

The boundary contour system (BCS), a massively parallel neural network model designed to perform early vision tasks, is discussed. Its tasks include boundary detection and segmentation. An arrangement of short-range competition and long-range cooperation among orientation-sensitive cells ensures a rapid, active sharpening of boundaries, including emergent boundaries (boundaries between regions which do not differ in global statistics of luminance distributions or have luminance patterns that are discontinuous or fuzzy). Patterns of activity in the BCS restrict the diffusion of activity in a complementary system called the feature contour system, which fills in features that are delineated by the BCS. The BCS is a dynamical system, not an algorithm. It is defined by an architecture of neurons and a set of differential equations that govern the neurons. Its implementation on an Intel iPSC/2 Hypercube with 16 vector processor nodes is discussed, as are the hardware used, integration techniques, data structures, memory management techniques, synchronization schemes, and other implementation issues.<>