  Forming perceptual groups and individuating objects in visual scenes is an
essential step towards visual intelligence. This ability is thought to arise in
the brain from computations implemented by bottom-up, horizontal, and top-down
connections between neurons. However, the relative contributions of these
connections to perceptual grouping are poorly understood. We address this
question by systematically evaluating neural network architectures featuring
combinations bottom-up, horizontal, and top-down connections on two synthetic
visual tasks, which stress low-level "Gestalt" vs. high-level object cues for
perceptual grouping. We show that increasing the difficulty of either task
strains learning for networks that rely solely on bottom-up connections.
Horizontal connections resolve straining on tasks with Gestalt cues by
supporting incremental grouping, whereas top-down connections rescue learning
on tasks with high-level object cues by modifying coarse predictions about the
position of the target object. Our findings dissociate the computational roles
of bottom-up, horizontal and top-down connectivity, and demonstrate how a model
featuring all of these interactions can more flexibly learn to form perceptual
groups.
