RI: Small: Statistical Perceptual Inference in Visual Cortical Neural Circuits

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This interdisciplinary research project seeks to elucidate the computational machinery and algorithms in our brain that enables us to perceive 3-dimensional surfaces of objects in visual scenes based on the 2D images projected on our retinae. The first conjecture of the project is that the neural circuitry underlying perceptual inference in our brain can be predicted by the statistical structures in our natural environment. To prove this conjecture, statistical studies on 3D natural scenes will be carried out and their predictions on neural connectivity will be compared with the functional connectivity and tuning properties of depth-sensitive neurons in the primate visual cortex obtained using large-scale multi-electrode recording techniques. This will provide deeper insights into how the brain represents and builds models of the structures of the external world to enable perceptual inference. To understand what such circuits can compute, the investigator conjectures that neural circuits realize a class of generative models in computer vision and computational neuroscience called Markov random fields and Boltzmann machine. This second conjecture will be evaluated by exploring the theoretical link between the neural circuits and these computational models, by comparing experimental neural observations with behaviors of these computational models, and by evaluating the computational performance of the inferred neural circuits in solving stereo computation and surface interpolation problems in real world data. The research combines techniques in machine and statistical learning, computer vision, neural networks and neurophysiology to dissect neural circuits from a functional perspective. By linking real circuits to a powerful class of computational models in statistical inference, the project will have broader impacts by providing novel evidence and fundamental insight to the neural mechanisms and computational algorithms underlying statistical perceptual inference in the brain. This interdisciplinary project will be a catalyst for the development of educational initiatives to bring bringing computer science and neuroscience together for undergraduates and graduate students, and to promote the awareness and involvement of students from multiple disciplines, including under-represented groups, in the field of computational neuroscience.