Reverse Engineering the Brain: Implications for Clinically Viable Brain Machine Interfaces
Karim G. Oweiss, University of Florida
Our ultimate understanding of the brain as a complex system is reflected in our ability to predict its dynamics in the normal state and eventually control these dynamics in the pathological state. Interest in reverse engineering the brain – the process of identifying its building blocks and revealing how they’re networked together – has been surging, primarily due to striking advances in neural interface technology intended to measure and manipulate brain dynamics at exceedingly high temporal and spatial resolutions, and to characterize the ever changing interplay between the brain’s structure and function.
In this talk, I will summarize the state of the art in brain machine interface (BMIs), and discuss how characterizing the neural dynamics underlying their operation is critical to understand the neural circuit mechanisms that mediate sensorimotor integration, as well as to engineer clinically viable BMIs that translate thoughts of neurologically impaired subjects into therapy and restore lost functions. In particular, I will demonstrate how characterizing these dynamics using advanced computational tools such as graph theory as well as experimental tools such as optogenetics is key to understand how neural ensembles orchestrate information processing in the brain. I will conclude by highlighting some key questions that have to be addressed for brain-machine interfaces to have a long lasting impact on basic and clinical neuroscience.