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 Visual perception is an
interactive process involving prediction, identification, classification,
and decision or reaction. My interest is in understanding how the visual
cortex takes local information about discontinuities (e.g., edges) and
forms a global percept. Specifically, how does the cortex integrate
distributed signals in order to identify correlations and patterns
among those discontinuities? The problem is compounded by the fact that
patterns and objects are typically embedded within an environment that
also contains structure and correlation. We use knowledge from natural
scene statistics, psychophysical data, and computer vision algorithms
to formulate hypotheses, and we employ rigorous statistical analyses on
simultaneous recordings from multiple neurons distributed across the cortical
network. Our results provide us with clues on how the brain is able to
segment and identify objects, as well as reveal properties of the
underlying cortical mechanisms.
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