A self-organizing neural network that learns to detect and represent visual depth from occlusion events

Abstract

Visual occlusion events constitute a major source of depth information. We have developed a neural network model that learns to detect and represent depth relations, after a period of exposure to motion sequences containing occlusion and disocclusion events. The network's learning is governed by a new set of learning and activation rules. The network develops two parallel opponent channels or ``chains'' of lateral excitatory connections for every resolvable motion trajectory. One channel, the ``On'' chain or ``visible'' chain, is activated when a moving stimulus is visible. The other channel, the ``Off'' chain or ``invisible'' chain, is activated when a formerly visible stimulus becomes invisible due to occlusion. The On chain carries a predictive modal representation of the visible stimulus. The Off chain carries a persistent, amodal representation that predicts the motion of the invisible stimulus. The new learning rule uses disinhibitory signals emitted from the On chain to trigger learning in the Off chain. The Off chain neurons learn to interact reciprocally with other neurons that indicate the presence of occluders. The interactions let the network predict the disappearance and reappearance of stimuli moving behind occluders, and they let the unexpected disappearance or appearance of stimuli excite the representation of an inferred occluder at that location. Two results that have emerged from this research suggest how visual systems may learn to represent visual depth information. First, a visual system can learn a nonmetric representation of the depth relations arising from occlusion events. Second, parallel opponent On and Off channels that represent both modal and amodal stimuli can also be learned through the same proce