SC7 Brain-Computer Interfaces to Probe Problem-Solving
Session 1: Introduction to motor neurophysiology. Behavior, single-unit physiology and population dynamics.
Session 2: Introduction to machine learning. Brain-computer interfaces as a specific application of machine learning. New tools for analyzing neural population activity.
Session 3: Studying learning and problem-solving using the BCI framework
Session 4: Continuation of prior lectures, overview of the BCI field, and brainstorming discussion.
Students will develop a working knowledge of brain-computer interfaces. This will include an ability to derive the fundamental equations, and an understanding of the tradeoffs choices inherent in BCI design. Students will be able to use modern tools of multineuronal data analysis. At very least, students will be equipped to read,
understand, and critically evaluate the emerging literature of brain-computer interfaces and multineuronal analysis.
Aaron Batista earned Bachelors’ degrees in Philosophy and Computer Engineering, and a Masters’ degree in Computer and Information Sciences at the University of Pennsylvania. He earned a PhD in Computation and Neural Systems at Caltech. Hispostdoctoral research was at Stanford University, in the laboratories of Bill Newsome and Krishna Shenoy. Aaron has been a faculty member at the University of Pittsburgh since 2007, where he is currently an Associate Professor. His home department is Bioengineering, and he is a member of the Center for the Neural Basis of Cognition.
Aaron’s laboratory studies the population neural mechanisms of sensory-motor integration and learning.