Our research goal is to understand how the brain builds an internal representation of the visual world and how is this representation updated as new information is acquired. How do individual neurons and networks in the visual cortex encode image features that vary dynamically between eye movements, and how does this encoding relate to visual behavior? To achieve this goal, we employ techniques that allow us to record simultaneously the activity of multiple neurons in the visual cortex of alert monkeys during specific behavioral tasks, in combination with human psychophysics and computational models of neural network function. Specific questions of interest in our laboratory are: How do rapid changes in the structure of images caused by eye movements influence how images are coded in the visual cortex? What is the relationship between feature encoding in early cortical areas (e.g., primary visual cortex), object encoding in higher cortical areas (e.g., inferotemporal cortex), and object perception? How do changes in the internal state of cortical networks (e.g., induced by behavioral or perceptual context) influence neural coding in visual cortex and the corresponding behavioral performance? We believe that our research on the neural coding of dynamic image representations has the potential to advance our understanding of the neuronal mechanisms underlying visual perception and learning, and, at the same time, help develop chronically-implantable human cortical prostheses to assist visually impaired people.
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Affiliations
Research Consortia
Gulf Coast Cluster for NeuroEngineering
Appointments
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Department / School
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Rochelle and Max Levit Distinguished Professor in the Neurosciences
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