Elucidating the Neural Control of Movement Using AI
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Bio
“Elucidating the Neural Control of Movement Using AI”
Shreya Saxena is broadly interested in the neural control of complex, coordinated behavior. She is an Assistant Professor at the University of Florida’s Department of Electrical and Computer Engineering. Before this, Shreya was a Swiss National Science Foundation Postdoctoral Fellow at Columbia University’s Zuckerman Mind Brain Behavior Institute. She did her PhD in the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology studying the closed-loop control of fast movements from a control theory perspective. Shreya received a B.S. in Mechanical Engineering from the Swiss Federal Institute of Technology (EPFL), and an M.S. in Biomedical Engineering from Johns Hopkins University. She is honored to have been selected as a Rising Star in both Electrical Engineering (2019) and Biomedical Engineering (2018).
Abstract
“Elucidating the Neural Control of Movement Using AI”
How does the motor cortex achieve generalizable and purposeful movements from the complex, nonlinear musculoskeletal system? Previous research in the field typically does not consider the biophysical underpinnings of the musculoskeletal system, and thus fails to elucidate the computational role of neural activity in driving the musculoskeletal system such that the body reaches a desired state. Here, I will present a deep reinforcement learning framework for training recurrent neural network controllers that act on anatomically accurate limb models such that they achieve desired movements. We apply this framework to kinematic and neural recordings made in macaques as they perform movements at different speeds. This framework for the control of the musculoskeletal system mimics biologically observed neural strategies, and enables hypothesis generation for prediction and analysis of novel movements and neural strategies.
Effectively modeling and quantifying behavior is essential for our understanding of the brain. Modeling behavior across different subjects and in a naturalistic setting remains a significant challenge in the field of behavioral quantification. We develop novel explainable AI methods for modeling continuously varying differences in behavior, which successfully represent distinct features of multi-subject and social behavior in an unsupervised manner. These methods are also successful at uncovering the relationships between recorded neural data and the ensuing behavior. I will end with future avenues on explainable AI methods for elucidating the neural control of movement.
