Statistical and Neural Basis of Surface Inference in Vision

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This is a multidisciplinary research program for investigating both the computational principles and the neural mechanisms underlying our visual perception of three-dimensional (3D) surfaces and shapes in the natural world. Its goal is to understand how surfaces of objects are inferred and represented in the brain. The general approach is first to discover the statistical regularities of patterns and structures in 3D natural scenes and to develop a computational framework for representing and inferring these structures from optical images; and second to test neurophysiologically the predictions generated by the computational framework on the neural basis of surface representation and inference. The fundamental hypothesis is that the visual system functions as a hierarchical probabilistic inference system in which the feedforward and feedback connections among the different visual areas in the cortical hierarchy serve to mediate two-way Bayesian belief propagation. In this framework, the brain is conjectured to actively construct a representation of the visual scene based on the retinal input as well as our prior knowledge and experience of the world. The investigator will carry out a novel statistical study of 3D natural scenes, develop efficient probabilistic computational algorithms for surface inference based on natural scene statistics, explore neural models for implementing such algorithms, and test neurophysiologically these models by recording and analyzing neuronal activity in the early visual areas of primate cerebral cortex. It is a tightly coupled interdisciplinary project that involves synergistic research in computer vision, computational neuroscience and systems neuroscience to address fundamental questions in these three fields. Understanding how the brain makes inference about the visual world will have a significant broad impact on neuroscience, clinical medicine and robotics. This integrated study of a hierarchical visual inference system and its associated probabilistic inference algorithms, rooted in natural scene statistics, will contribute to the foundation for building a new generation of flexible and intelligent robotic vision systems. Such systems will be able to learn and adapt to the statistical regularities of a changing environment and make inferences based on scene contexts. The proposed research program also provides an unique educational vehicle of interdisciplinary training to graduate and undergraduate students that will serve as a catalyst to integrate computer science research and biological research in the scientific community at large.