Visual recognition of objects, scenes and people around us depends on the visual cortex, one of the largest and most complex parts of the primate brain. This area also presents one of the major puzzles in cognitive sciences: What is the role of the cortical back-projections, that is, links from the secondary areas back into the primary areas? In this project, Drs. Tomaso Poggio and Earl Miller try to interpret the neural computations underlying complex visual recognition tasks. Their studies will help to transfer knowledge gained from animal models of visual perception toward the understanding of higher cognitive visual processes in humans. A very recent computational theory of the primate object recognition system agrees with a variety of physiological findings in different visual areas, such as V1 (primary visual cortex), V4 (visual area 4), IT (inferotemporal cortex), and PFC (prefrontal cortex). Even more surprisingly, it mimics human behavioral performance on rapid categorization of complex natural images, and performs, as well as several state-of-the-art computer vision systems on difficult recognition tasks. Considering that the model is able to account for rapid object recognition, and that it currently only uses feedforward processing, a significant puzzle concerns the computational and functional role of the abundant anatomical back-projections known to exist in cortex. The proposed project takes a two-pronged approach toward finding their function. First, experiments with the computational model of the ventral stream will provide useful insights for understanding the possible functions of the back-projections. Second, experiments with multi-unit recordings in macaques will characterize top-down effects and their timing in several different recognition tasks at the level of IT and PFC. The analysis will make use of a recently developed automatic classification technique that relates brain activity with individual visual objects. By combining results from the modeling and experimentation, computational explanations for the cognitive role of the feedback processing can be tested.
Understanding the function of back-projections in vision will help us understand the neural basis of vision itself, but it will also help us understand the global design of the brain. The intricacy of this organ continues to amaze us, and getting a handle on its mechanism through a relatively well-understood area like vision is a promising avenue for expanding our appreciation of the whole system. A further goal is to show that the interaction between computational theories, in particular of vision, and experiments will make it easier to comprehend brain functions. Such advances help us appreciate the normal function of the brain and allow us better means of helping when it does not function normally, of making machines see better, and of bringing new approaches to robotics.
