David J. Freedman, PhD

Stahl Professor of Neurobiology in the Wallman Society of Fellows 

Department of Neurobiology and the College

Chair, Department of Neurobiology

Member, Neuroscience Institute

Faculty Affiliate, Data Science institute

The University of Chicago

Title:

The Brain's Oculomotor Networks are Recruited to Mediate Abstract Cognitive Behaviors

Abstract

Humans and other animals are adept at learning to perform cognitively-demanding behavioral tasks. Neurophysiological recordings in non-human primates during such tasks find that task-related cognitive variables are encoded across a wide network of brain regions, but particularly strongly so in core oculomotor brain regions such as the frontal eye field, superior colliculus, and posterior parietal cortex--even in tasks which require gaze fixation and in which monkeys indicate their decision with hand, rather than eye, movements. This talk will discuss the causal significance of the observed cognitive encoding in oculomotor circuits, as well as new evidence for cognitively modulated incidental gaze shifts akin to a "poker tell" observed in human subjects.

Bio:

Dr. David J. Freedman is The Stahl Professor of Neurobiology in the Wallman Society of Fellows and Chair of The Department of Neurobiology at The University of Chicago, where is also a member of the graduate programs in Neurobiology and Computational Neuroscience. Dr. Freedman earned his Bachelor’s degree from the University of Rochester, and his Ph.D. in Systems Neuroscience from the Massachusetts Institute of Technology (MIT) working in Earl Miller’s laboratory. He completed postdoctoral fellowships at MIT with Earl Miller and Harvard Medical School with John Assad before joining the faculty at The University of Chicago in 2008. The central goal of Dr. Freedman’s research is to understand the brain mechanisms of higher order cognitive and perceptual functions, such as learning, memory, and decision making. His laboratory uses advanced neurophysiological techniques to monitor the activity patterns of populations of neurons in multiple brain areas during visual learning, memory and recognition tasks. His group also employs modeling and machine learning approaches to explore the computations performed by neural networks in order to perform cognitively demanding tasks.