Xiao-Jing Wang

Computational Neuroscience, Decision-Mmaking and Working Memory, Neural Circuits

Research in my group aims at understanding dynamical behavior and function of neural circuits. Using theoretical and modeling approaches, in close collaboration with experimentalists, we investigate the neural mechanisms and computational principles of cognitive processes, such as decision-making (how we make a choice among multiple options) and working memory (how our brain holds and manipulates information "online" in the absence of sensory stimulation).

I obtained my Ph. D. degree in Theoretical Physics, from the Free University of Brussels, in 1987 when I switched to the then nascent field of Computational Neuroscience. I was on the faculty at University of Pittsburgh, Brandeis University and Yale University; I was also visiting professor at École Normale Supérieure in Paris and Tsinghua University in Beijing. Recently, I moved from Yale to join the Center for Neural Science at New York University.

My group has been focused on the prefrontal cortex (PFC), which is often called "the CEO of the brain". I am interested in identifying circuit properties that enable PFC to subserve higher cognitive functions, in contrast to early sensory processing. We found that a local circuit endowed with strong but slow recurrent dynamics ("reverberation") is well suited for both decision-making and working memory, suggesting a canonical "cognitive-type" neural circuit. Mathematically, such circuits are described as "attractor networks" that are characterized by powerful feedback mechanisms, long transients as well as self-sustained persistent activity. This finding led us to investigate all sorts of decision processes, including reward-based economic choice behavior, categorization, inhibitory control of action selection, attention switching, probabilistic inference. A recent collaborative work offers a theoretical explanation, supported by single-neuron data from behaving monkeys, of a common and perplexing observation that neural activities in the PFC display a high degree of mixed-selectivity and heterogeneity. Furthermore, we are keen to learn why the brain exhibits such a rich diversity of inhibitory interneuron subtypes, and their roles in tuning, normalization, competition, rhythmic synchronization. Finally, in a new field called "Computational Psychiatry" and in collaboration with psychiatrists, we use our models to examine cellular and circuit abnormalities that may be causally linked to cognitive deficits associated with mental disorders such as schizophrenia.

Recently, we have begun to investigate large-scale brain circuit models, with the long-term goal to develop a theoretical framework and computational platform to explore how brain sub-networks dedicated to different "building blocks of cognition" (perceptual judgment, valuation, representations of task rule and uncertainty, inhibitory control of response, etc) work together to underlie flexible behavior.