CNI Seminar: Byron Yu

Byron Yu
Gerard G. Elia Career Development Professor
Electrical & Computer Engineering and Biomedical Engineering
Carnegie Mellon University

 

Brain-computer interfaces for basic science

The manner in which neural activity unfolds over time is thought to be central to sensory, motor, and cognitive functions in the brain. Network models have long posited that the brain’s computations involve time courses of activity that are shaped by the underlying network. A prediction from this view is that the activity time courses should be difficult to violate. In this talk, I will describe how we used a brain-computer interface (BCI) to challenge monkeys to violate naturally-occurring time courses of neural population activity.  This included challenging animals to traverse the natural time course of neural activity in a time-reversed manner.  We found that animals were unable to violate the natural time courses of neural activity when directly challenged to do so.  These results provide empirical support for the view that activity time courses observed in the brain indeed reflect the underlying network-level computational mechanisms that they are believed to implement.

 

Byron Yu is the Gerard G. Elia Career Development Professor in Electrical & Computer Engineering and Biomedical Engineering at Carnegie Mellon University.  He received the B.S. degree in Electrical Engineering and Computer Sciences from the University of California, Berkeley, and the M.S. and Ph.D. degrees in Electrical Engineering from Stanford University. He was a postdoctoral fellow jointly in Electrical Engineering and Neuroscience at Stanford University and at the Gatsby Computational Neuroscience Unit, University College London.  He is broadly interested in how large populations of neurons process information, from encoding sensory stimuli to driving motor actions.  His group develops and applies novel statistical algorithms and uses brain-computer interfaces to study brain function.

A pizza lunch will be served.