Date(s) - 03/21/2022
3:00 pm - 4:00 pm
Join Zoom Meeting:
Launch Zoom Meeting
Brain-machine interfaces (BMIs) change how the brain sends and receives information from the environment, opening new ways to treat neurological disorders and study brain function. For instance, motor BMIs directly map neural activity to the movements of an external device to restore movements to paralyzed people. Recent work highlights that BMIs do not simply “decode” subjects’ intentions—they effectively create a new motor system the brain learns to control. Insights into sensorimotor learning and control in BMIs will be critical for improving BMI performance and usability, and may also shed light on basic principles of neural computation. In this talk, I’ll first present a study where we leveraged the unique properties of BMI to probe the roles of feed-forward and feedback sensorimotor control in BMI. Our study sheds light on sensorimotor control mechanisms, and in turn led to state-of-the-art neural interface performance. I’ll then discuss the role of learning in motor BMIs and some new directions developing computational and neurophysiological tools to actively shape or “engineer” learning and influence neural encoding to optimize BMI performance.
Bio: Dr. Amy Orsborn is a Clare Boothe Luce Assistant Professor in Electrical & Computer Engineering and Bioengineering at the University of Washington. She’s also a core staff scientist at the Washington National Primate Research Center. She works at the intersection of engineering and neuroscience to develop neural interfaces to restore motor function. Among her honors, she received a L’Oreal USA for Women in Science postdoctoral award, the L’Oreal USA Changing The Face of STEM award, a Google Faculty Research Award, an Interdisciplinary Rehabilitation Engineering research fellowship, and a pilot award from the Simons Foundation Collaboration on the Global Brain. She completed her Ph.D. at the UC Berkeley/UCSF Joint Graduate Program in Bioengineering, and was a postdoctoral researcher at NYU’s Center for Neural Science.