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Disambiguating Visual Motion Through Contextual Feedback Modulation

pierre{at}neuro.informatik.uni-ulm.de, Department of Neural Information Processing, University of Ulm, D-89069 Ulm, Germany

Heiko Neumann

hneumann{at}neuro.informatik.uni-ulm.de, Department of Neural Information Processing, University of Ulm, D-89069 Ulm, Germany

Motion of an extended boundary can be measured locally by neurons only orthogonal to its orientation (aperture problem) while this ambiguity is resolved for localized image features, such as corners or nonocclusion junctions. The integration of local motion signals sampled along the outline of a moving form reveals the object velocity. We propose a new model of V1-MT feedforward and feedback processing in which localized V1 motion signals are integrated along the feedforward path by model MT cells. Top-down feedback from MT cells in turn emphasizes model V1 motion activities of matching velocity by excitatory modulation and thus realizes an attentional gating mechanism. The model dynamics implement a guided filling-in process to disambiguate motion signals through biased on-center, off-surround competition. Our model makes predictions concerning the time course of cells in area MT and V1 and the disambiguation process of activity patterns in these areas and serves as a means to link physiological mechanisms with perceptual behavior. We further demonstrate that our model also successfully processes natural image sequences.

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