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More than a century's worth of behavioural investigations have demonstrated that animals and humans process sensory information close to optimally, often employing subtle and powerful algorithms to do so. Our understanding of these computations at the neural level is, by contrast, quite simplistic. The goal of research in my group is to help bridge this gap, using both theoretical and data-driven approaches to understand how information is represented in neural systems, and how this representation underlies computation and learning. On the one hand, we collaborate closely with physiologists to advance the technology of neural data collection and analysis. These studies have the potential to introduce powerful new theoretically-motivated ways of looking at neural data. At the same time, we examine neural information representation and perceptual behaviour from a more theoretical point of view, addressing questions of how the brain might encode the richness of information needed to explain perceptual capabilities, what purpose might be served by adaptation in neural activities, and how experience-driven plasticity in representations is related to perceptual learning. Both the data analytic and the theoretical aspects of our neuroscience research are closely connected to the field of machine learning, which provides the tools needed for the first, and a structural framework for the second.