Statistical methods for analysing maps in the visual brain

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This project aims to apply Gaussian process methods, a Bayesian approach for data analysis, to analyse data from brain imaging experiments. Discovering the principles of functional brain architecture requires analysing data from functional imaging technologies. However, these technologies produce very noisy data which is difficult to interpret. This project will apply Gaussian process methods to study data from optical imaging and functional magnetic resonance imaging of the visual brain. This is expected to reveal critical information about how normal brain structure changes with development and sensory experience. The statistical methods developed should be applicable within and beyond neuroscience, and may ultimately help improve the diagnosis of human health disorders.