Merging Basic Research in Data-Driven Cognitive Neuroscience with Real-life Application

Friday, April 12, 2013 - 11:00am - 12:00pm EDT

  • Location:Jacobs Academic Building, JAB 473
    Six MetroTech Center, BROOKLYN, New York, US

Speaker: Jason Sherwin

Host Faculty: Professor Jon Viventi

Abstract

Finding the overlap between basic research and real-life application remains a challenge for any branch of engineering. But in the budding discipline of neural engineering, insights from basic research have ready use in as many disciplines as the human neural system is capable of operating. The challenge for us as engineers is to find those applications. In my talk, I will address how data-driven cognitive neuroscience can be used to gain insights into how the neural system functions in a few of such disciplines, namely music, combat and sports. Along the way, I will demonstrate the crucial role that machine learning and optimization play in creating increasingly robust decoding of neuroimaging data. Following the thesis that the neural system can be a guide to handling complex data in these and other environments, I will show how these results have ready application to the disciplines in which they were found and how they create a unique opportunity for both academia and industry to come together in a timely blend of i2e (invention, innovation and entrepreneurship).

About the Speaker

Jason Sherwin, Ph.D. holds dual appointments as a post-doctoral research scientist at the Columbia University in the City of New York and as an Oak Ridge Associated Universities post-doctoral fellow at the U.S. Army Research Laboratory (ARL). He also serves as the Managing Editor of the IEEE Transactions on Neural Systems and Rehabilitation Engineering. In addition to scholarly pursuits, he is active in the entrepreneurial community of New York, having served as consultant to Neuromatters, LLC and the City College of New York, each in their own respective contracts with the Defense Advanced Research Projects Agency (DARPA). His research covers perceptual decision-making in real-world environments, using en vivo neuroimaging and machine learning algorithms to improve the analysis of neural data in these complex and dynamic environments.