Colloquium on the Brain and Cognition with David Freedman, PhD, University of Chicago

Thursday, October 12 at 4:00pm

Singleton Auditorium (46-3002)

Zoom (Open to the Public): https://mit.zoom.us/j/93741152917

Neural Circuits and Computations for Abstract Decision Making in the Primate Brain

Humans and many other animals have a remarkable ability to recognize the behavioral significance of incoming sensory stimuli, and to make flexible decisions about those stimuli according to the demands of the current behavioral context. This requires interaction and coordination among a hierarchy of cortical and subcortical areas, to transform stimulus encoding in sensory cortical areas into task-related cognitive encoding in higher order processing stages. This talk will describe our progress in understanding the neural basis of abstract decision making in the primate brain, studies using large-scale recording of neuronal populations as well as reversible inactivation across sensory, cognitive, and motor brain regions including the posterior parietal cortex, prefrontal cortex, and superior colliculus. The talk will also discuss progress in understanding putative computations underlying abstract decisions in biologically-inspired artificial neural networks trained to perform the same kinds of tasks as in our physiological experiments.

Dr. David Freedman is a Professor in The Department of Neurobiology at The University of Chicago, and is 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 both MIT and Harvard Medical School 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 learning and memory. His lab investigates how encoding of basic visual features (like edges, contrast, motion, and color) in sensory brain areas is transformed by learning into more abstract and meaningful representations that reflect the behavioral significance of stimuli. 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. Dr. Freedman has received numerous honors and awards including the Troland Research Award from the National Academy of Sciences, the Vannevar Bush Faculty Fellowship from the US Department of Defense, Fellowships from the Sloan and McKnight Foundations, a NSF CAREER award, and the Outstanding Graduate Teaching and Mentorship Award from The University of Chicago.