Dissecting Learning and Control in Brain-Machine Interfaces
New York University
March 3, 2016
Foege N130A, Wallace H. Coulter Seminar Room
Direct interfaces with the brain provide exciting new ways to restore and repair neurological function. For instance, motor Brain-Machine Interfaces (BMIs) can bypass a paralyzed person’s injury by repurpose intact portions of their brain to control movements. Recent work shows that BMIs do not simply “decode” subjects’ intentions—they create new systems subjects learn to control. To improve BMI performance and usability, we must therefore better understand learning and control in these systems. I will present recent work exploring BMIs as “multi-learner” systems, where both the brain and decoding algorithm adapt. We find that neuroplasticity and adaptive decoding can combine synergistically to yield skilled control, and this combination can solve real-world limitations for motor prostheses. I will also present new directions aimed to understand the neural mechanisms of learning in BMIs by probing neural circuits across different spatial scales. Together, this work will elucidate how the brain interacts with a BMI to achieve control.
Amy L. Orsborn works at the interface of engineering and neuroscience to study learning, and to leverage neuroplasticity to restore motor function to persons with disabilities. She received her bachelors in engineering physics from Case Western Reserve University, and her doctorate degree from the UC Berkeley – UCSF graduate program in bioengineering. Her Ph.D. research with Dr. Jose M. Carmena explored multi-learner brain-machine interface (BMI) systems. Her work specifically highlighted the importance of learning for clinical neuroprostheses and demonstrated the power of BMI to study learning. She is currently a postdoctoral researcher in the Center for Neural Science at New York University. Working with Dr. Bijan Pesaran, she is developing platforms for multi-scale, multi-modal brain recording and stimulation in non-human primates to map large-scale neural circuits. Combining circuit mapping with BMI will enable new insights into how the brain learns complex tasks, and in turn enable new therapies for neurological damage and disease. Dr. Orsborn has received fellowships from the National Science Foundation and American Heart Association, and was recently nominated for the Blavatnik Regional Award for Young Scientists.