In the Mood: Deciphering complex brain signals

LLNL Scientist(s)
Alan Kaplan
Teacher
Erin McKay
Teachers School
Tracy HS

Abstract: The human brain contains approximately 86 billion neurons, and 100 trillion connections between those neurons. Despite our inability to image each neuron and determine their exact connective patterns, several approaches for noninvasive imaging of the living brain have been developed and utilized to great benefit. In this talk, we will explore the immense landscape of the human brain and quantify the brain in terms of data flow. Then we will describe engineering applications of recorded electrophysiological data. We will also explore methods for analyzing such data to determine the pattern of signals that arise during various activities and mood states.

Bios:
Alan Kaplan is a research engineer and group leader for Computer Vision at the LLNL. He received his Ph.D. in Electrical Engineering from Washington University in St. Louis in 2011. His research focuses on the development of methods for modeling complex data. He has worked on data driven approaches for nuclear particle detectors, imaging for real-time quality assurance, and neuroscience. His work in neuroscience involves the analysis of electrocorticographic signals, large-scale neuroimaging based connectomics, and predictive methods for traumatic brain injury. He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and Society for Industrial and Applied Mathematics (SIAM). 

Katherine Huang teaches Honors Anatomy and Physiology, and Accelerated Biotechnology and Research at Dougherty Valley High School in San Ramon. She received her B.S. in Biology at UCLA and MAT at UC Irvine. She also works in the Science Education Program at LLNL, having instructed the Waksman Student Scholars Program, which works closely with Rutgers University to sequence novel duckweed DNA in hopes of discovering proteins for uses such as bioremediation.