Experimental evidence from neurophysiological recordings in the middle temporal (MT) area of the macaque monkey suggests that motion-selective cells can use disparity information to separate motion signals that originate from different depths. This finding of a cross-talk between different visual channels has implications for the understanding of the processing of motion in the primate visual system and especially for behavioral tasks requiring the determination of global motion. In this paper, the consequences for the analysis of optic flow fields are explored. A network model is presented that effectively uses the disparity sensitivity of MT-like neurons for the reduction of noise in optic flow fields. Simulations reproduce the recent psychophysical finding that the robustness of the human optic flow processing system is improved by stereoscopic depth information, but that the use of this information depends on the structure of the visual environment.

Interest in the processing of optic flow has increased recently in both the neurophysiological and the psychophysical communities. We have designed a neural network model of the visual motion pathway in higher mammals that detects the direction of heading from optic flow. The model is a neural implementation of the subspace algorithm introduced by Heeger and Jepson (1990). We have tested the network in simulations that are closely related to psychophysical and neurophysiological experiments and show that our results are consistent with recent data from both fields. The network reproduces some key properties of human ego-motion perception. At the same time, it produces neurons that are selective for different components of ego-motion flow fields, such as expansions and rotations. These properties are reminiscent of a subclass of neurons in cortical area MSTd, the triple-component neurons. We propose that the output of such neurons could be used to generate a computational map of heading directions in or beyond MST.