Simulating biological vision with hybrid neural networks

discrimination of objects using cues derived from object interposition. We construct a model of object segmentation using hybrid neural networks—distributed parallel systems consisting of neural units modeled at different levels af abstraction. We show that such networks are particularly useful for systems which can be modeled using the combined top-down/bottom-up approach. Our hybrid model is capable of discriminating objects and stratifying them in relative depth. In addition, our system can account for several classes of human perceptual phenomena, such as illusory contours. We