Abstract

Complex cells in the striate cortex exhibit extensive spatiotemporal nonlinearities, presumably due to a convergence of various subunits. Because these subunits essentially determine many aspects of a complex cell receptive field (RF), such as tuning for orientation, spatial frequency, and binocular disparity, examination of the RF properties of subunits is important for understanding functional roles of complex cells. Although monocular aspects of these subunits have been studied, little is known about their binocular properties. Using a sophisticated RF mapping technique that employs binary m-sequences, we have examined binocular interactions exhibited by complex cells in the cat's striate cortex and the binocular RF properties of their underlying functional subunits. We find that binocular interaction RFs of complex cells exhibit subregions that are elongated along the frontoparallel axis at different binocular disparities. Therefore responses of complex cells are largely independent of monocular stimulus position or phase as long as the binocular disparity of the stimulus is kept constant. The binocular interaction RF is well described by a sum of binocular interaction RFs of underlying functional subunits, which exhibit simple cell-like RFs and a preference for different monocular phases but the same binocular disparity. For more than half of the complex cells examined, subunits of each cell are consistent with the characteristics specified by an energy model, with respect to the number of subunits as well as relationships between the subunit properties. Subunits exhibit RF binocular disparities that are largely consistent with a phase mechanism for encoding binocular disparity. These results indicate that binocular interactions of complex cells are derived from simple cell-like subunits, which exhibit multiplicative binocular interactions. Therefore binocular interactions of complex cells are also multiplicative. This suggests that complex cells compute something analogous to an interocular cross-correlation of images for a local region of visual space. The result of this computation can be used for solving the stereo correspondence problem.

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