The closed-loop operation of brain-machine interfaces (BMI) provides a framework to study the mechanisms behind neural control through a restricted output channel, with emerging clinical applications to stroke, degenerative disease, and trauma. Despite significant empirically-driven improvements in closed-loop BMI systems, a fundamental, experimentally-validated theory of closed-loop BMI operation is lacking. In this talk, we propose a compact model based on stochastic optimal control to describe the brain in skillfully operating canonical decoding algorithms. The model generates goal-directed BMI movements with sensory feedback and intrinsically noisy neural output signals, reproducing various experimentally-validated phenomena. Analysis of this model reveals connections between system structure and system behavior in progress towards uncovering the foundational principles of BMI design.
Joint work with Manuel Lagang (B.S. Computer Science, Caltech, 2011), presently a graduate student at UIUC. Funded in part by the American Heart Association Scientist Development Grant and the UCLA Department of Radiology.