On October 25th, we are honored to welcome Xiao-Jing Wang at Sorbonne Université.
Xiao-Jing Wang, distinguished Global Professor of Neural Science, director of the Swartz Center for Theoretical Neuroscience at New York University, will give a conference at 4pm on Pierre et Marie Campus (amphitheater 25).
Today’s remarkably successful AI systems roughly correspond to the biological systems of perception. By contrast mental life and behavioral flexibility depend on other parts of the brain, especially the prefrontal cortex (PFC, often called the “CEO of the brain”). Here I will discuss some experimental and computational work designed to elucidate “cognitive-type” neural circuits exemplified by the PFC. In particular, I will present a recurrent network model that learns to carry out many cognitive tasks involving working memory, decision-making, categorization and control of motor responses. This line of research motivated us to investigate how the brain utilizes previously acquired knowledge to accelerate learning in solving a new problem (learning-to-learn). Both rule-based multi-tasking and learning-to-learn are frontier topics in the field of machine learning, therefore bridging the brain and the AI.
Biosketch: Xiao-Jing Wang is Distinguished Global Professor of Neural Science, director of the Swartz Center for Theoretical Neuroscience at New York University. Dr. Wang’s research focuses on theory and neural mechanisms of cognitive functions such as working memory and decision-making, with a special interest in the prefrontal cortex. More recently, his group developed biologically-and connectome-based modeling of large-scale multi-regional brain circuits. Among awards he has received are Guggenheim Fellowship, Swartz Prize for Theoretical and Computational Neuroscience, Goldman-Rakic Prize for Outstanding Achievement in Cognitive Neuroscience, and election to the Royal Academy of Belgium.