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Algorithms for Understanding Motor Cortical Processing and Neural Prosthetic Systems

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Our seemingly effortless ability to make coordinated movements belies the sophisticated computational machinery at work in our nervous system. In recent years, the field of systems neuroscience has been dramatically expanding the complexity of its data acquisition technologies and experiments. This shift seeks to deliver a much deeper understanding of cortical processing and a much improved ability to control neural prosthetic devices (also called brain-machine interfaces). Paying this off, however, requires analytical methods that can exploit this changing paradigm. I will discuss a few examples of our algorithmic developments for understanding cortical processing and for applied prosthesis work. I will focus particularly on our efforts to use Gaussian Processes to extract population-level signatures of neural activity in the brain’s motor system. I will discuss future directions of my algorithmic work, particularly as it pertains to neural prosthetic systems, and I will also point to the broader implications this work should have for computer science, engineering, and neuroscience.

This talk is part of the Computational and Biological Learning Seminar Series series.

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