Date: 8 January 2026 (Thursday)
Time: 4:00 pm – 5:00 pm
Venue: Mrs Chen Yang Foo Oi Telemedicine Centre, 2/F, William M.W. Mong Block, 21 Sassoon Road
Host: Professor Michael Hӓusser

Xiao-Jing Wang is a Distinguished Global Professor of Neural Science at New York University, with a PhD in Theoretical Physics. Previously he was Professor at Yale University School of Medicine. Using theory and computational modeling, Dr. Wang’s research focuses on neural circuit mechanisms of cognitive functions such as decision-making, with a special interest in the prefrontal cortex which plays a central role in intelligence and executive control of behavior. He is one of the founders of the nascent field of Computational Psychiatry. More recently, his group developed connectome-based modeling of large-scale brain circuits to investigate whole-brain dynamics and distributed cognition. His research bridges neuroscience, artificial intelligence and psychiatry. Dr. Wang is a recipient of Alfred P. Sloan Research Fellowship, Guggenheim Fellowship, Swartz Prize for Theoretical and Computational Neuroscience, Goldman-Rakic Prize for Outstanding Achievement in Cognitive Neuroscience, and he was elected to the Royal Academy of Belgium. Dr. Wang’s H-index is 108, the total number of citations=51,600 as of today. He was recognized as Highly Cited Researcher by Clarivate Analytics, Web of Science Group in 2021 and 2024. He is the author of “Theoretical Neuroscience: Understanding Cognition” published by CRC/Taylor & Francis (2025), 561 pages. Currently, he is a visiting professor at Stanford University.

Dr. Xiao-Jing Wang was interviewed by Global People magazine:

http://paper.people.com.cn/hqrw/html/2013-09/16/content_1300621.htm

I will begin with a brief introduction to the cross-disciplinary field of theoretical/computational neuroscience, and its interplay with modern AI (“NeuroAI”). Focusing on the prefrontal cortex (PFC) at the core of intelligence, I will illustrate how theory and experiments work together to understand “cognitive-type” neural circuits for flexible decision making and compositionality/learning-to-learn. I will then discuss connectome-based modeling of the large-scale neocortex in monkeys and mice, highlighting the general principle of macroscopic gradients of synaptic excitation and inhibition across the neocortex. This line of research suggests that a new concept dubbed “bifurcation in space” can explain functional modularity/specialization compatible with selectively distributed neural coding and processes in the brain.