A computational model of perceptual reversal alternating between two interpretations is presented. Initially, the model represents the ambiguous state of a reversible picture, such as the bistable face-vase image. The internal state of the network evolves to settle into a stable state, which corresponds to one of two alternatives. Top-down feedback proves a deciding factor in leading the system into a modeled perceptual state over the time course. At any given time, top-down input from temporal associative memory provides contextual modulation of bottom-up input in the network. The model accounts for the role of top-down knowledge in resolving perceptual ambiguity as well as reversibility from one state to another.

Key words: Perceptual reversal, Figure-ground, Top-down feedback, Multi-stability