Neural mechanisms of active vision in the fovea

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Neural mechanisms of active vision in the fovea In many ways human vision is like a camera, with a lens that forms an image on a spatially arranged sensor (the retina). However, it is unlike a camera because the sensor has uneven sampling and is constantly moving with the eyes. Recent behavioral and theoretical work suggest these eye movements serve a faciliatory role in high acuity vision – where the eye movements are part of the computations and enhance spatial resolution. However, the neurophysiological mechanisms to support this facilitation remain unknown. More broadly, little is known about the neural mechanisms that integrate across the retinal motion generated by eye movements, especially in the central visual field (the fovea). This is particularly important because over 8 million Americans suffer from central vision loss due to retinal disorders. Even if the retinal signals could be repaired, it is imperative to understand how the brain reads out foveal signals to ensure recovery of high-acuity visual processing, and fixational eye movements are a part of that process. The proposed career development plan aims to address these questions by measuring visual processing in the foveal representation of primary visual cortex (V1) during natural visual behavior. This proposal uses custom high-resolution eye-tracking, a novel visual foraging paradigm, largescale neurophysiology, and state-of-the-art machine learning to make these measurements possible. The proposed research will not only generate fundamental understanding of how eye-movements facilitate visual processing, but also will integrate the experimental and theoretical tools required to support neurophysiological studies of active visual processing without a loss of rigor or detail. The candidate has extensive expertise in awake- behaving neurophysiology and computational modeling and the training plan is designed to support his further training in statistical modeling, high-resolution eye-tracking, and modern machine-learning techniques for analyzing neural population data. The primary mentor, Dr. Daniel Butts, is a world expert in statistical models of neural activity during active vision; Co-mentor, Dr. Michele Rucci, is a world leader in high-resolution eye tracking and theoretical approaches to active vision; and Co-mentor, Dr. Jude Mitchell, is a pioneer in establishing the marmoset model of visual neuroscience and an expert in neurophysiology of visual attention. Together, they will provide the guidance to establish the candidate’s transition to a successful independent research career.