研究方向
 计算神经科学、决策及工作记忆

个人简历

汪小京教授现任纽约大学神经科学教授，及物理与数学兼职教授，并任纽约大学斯沃茨理论神经科学中心联合主任。他同时是上海纽约大学科研副校长、华东师范大学- 纽约大学脑与认知科学联合研究中心（上海纽约大学）主任。2012年至2014年，汪教授担任上海纽约大学首位教务长。汪教授于2012年加入纽约大学，此前曾任耶鲁大学神经生物学教授、耶鲁大学斯沃茨理论神经科学中心主任、物理、应用数学系与心理学系兼职教授。

汪教授是理论与计算神经科学专家，研究重点是认知功能的脑机制，尤其以在工作记忆的细胞基础、决策的神经机制、大脑抑制神经元网络的研究而著称。他的团队开创了被称为“大脑CEO”的前额叶皮质神经网络模型研究。最近，汪教授的团队正创建大型脑的神经系统仿真模型，来深入研究认知、行为的脑机制和计算原理。

汪教授曾以最优成绩获得比利时布鲁塞尔大学的物理学学士学位与博士学位。他是阿尔弗雷德·斯隆研究学者奖、美国国家科学基金会CAREER奖、古根海姆纪念基金会学者奖、斯坦福大学行为科学高等研究中心学者奖获得者。此外，汪教授还是美国科学促进会会士。

Xiao-Jing Wang

ISDM Associate Director,
Associate Vice Chancellor for Research,
Professor of Neural Science

Xiao-Jing Wang is the Associate Vice Chancellor for Research at NYU Shanghai, and Professor of Neural Science at New York University. Before joining NYU in the fall of 2012, Wang was Professor of Neurobiology at Yale University. At Yale he also served as the Director of the Swartz Center for Theoretical Neuroscience, and held secondary faculty appointments in Physics, Applied Mathematics and Psychology.

Wang is an expert on the neurobiology of executive and cognitive functions. His group has pioneered neural circuit models of the prefrontal cortex, which is often called the “CEO of the brain”. In particular, Wang is known for his work on the cellular basis of short-term memory, neural mechanisms for decision-making, communication and synchronization through inhibitory neurons in the brain. His research group is now embarking on a new initiative of developing neurobiologically-realistic large-scale brain circuit models of cognitively-controlled flexible behavior.

Wang received his Bachelor of Science and Doctor of Philosophy in Physics, both with the highest distinction, from the University of Brussels, Belgium. He is a recipient of an Alfred P. Sloan Research Fellowship, a National Science Foundation CAREER Award, a John Simon Guggenheim Memorial Foundation Fellowship, and the Chinese Government’s 1000 Talent Award. Wang is also a Fellow of the American Association for the Advancement of Science.

Xiao-Jing Wang

Professor of Neural Science
Education

    Ph.D. 1987 Free University of Brussels

Areas of Interest
Computational Neuroscience Dynamical Systems Theory and Statistical Physics
Research Overview
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).

neural circuits

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.

chart results

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.