Zhewei Zhang, 


Cognitive/System/Computational neuronscience


My name is Zhewei Zhang, and I am currently a post-doctoral fellow at the National Institutes of Health (NIH) working with Dr. Geoffrey Schoenbaum. My research focuses on the neural circuitry involved in encoding sensory prediction errors and reward prediction errors. I completed my Ph.D. under the supervision of Tianming Yang at the Institute of Neuroscience (ION), Chinese Academy of Sciences.

Maximizing gains while minimizing losses is a fundamental principle of decision-making and learning for all organisms. However, in the real world, the relationships between events and their corresponding reward outcomes can be complex and dynamic. To exhibit adaptive behavior, animals must be able to learn and track these contingencies, and this ability is the foundation of decision-making. Thus, understanding the neural mechanisms underlying this process is critical. My research combines experimental and theoretical approaches to study these mechanisms and shed light on the neural basis of flexible and adaptive behaviors.

zhzhewei36 at gmail dot com

zhewei.zhang at nih dot gov


Jun. 2021 -  now Post-Doctoral Fellow, National Institute on Drug Abuse, National Institutes of Health

      • Advisor: Geoffrey Schoenbaum, MD, Ph.D.

Sep. 2014 - Jun. 2021      Ph.D., Institute of Neuroscience, Chinese Academy of Sciences.

      • Advisor: Tianming Yang, Ph.D. 

Sep. 2010 - Jun. 2014   B.S.,  School of Life Science, Sun Yat-Sen University.


Research Articles

Zhang Z, Yin C, & Yang T*.  Evidence accumulation occurs locally in the parietal cortex. Nature Communications, 2022. 13, 4426. 

Zhang Z, Cheng H, & Yang T*. A Recurrent Neural Network Model for Flexible and Adaptive Decision Making based on Sequence Learning [J]. PLOS Computational Biology, 2020, 16(11): e1008342.

Liu D, Deng J, Zhang Z, Zhang Z Y, Sun Y G, Yang T, & Yao H*. Orbitofrontal control of visual cortex gain promotes visual associative learning [J]. Nature Communications, 2020. 11(1): 1-14.

Zhang Z, Cheng Z, Lin Z, Nie C, & Yang T*. A neural network model for the orbitofrontal cortex and task space acquisition during reinforcement learning [J]. PLOS Computational Biology, 2018, 14(1): e1005925.

Other Publications

Xie Y, & Zhang Z*. Understanding commonalities and discrepancies between feature and spatial attention effect in the context of a normalization model. Journal of Neuroscience 2020, 40 (5) 955-957


Zhang Z, Takahashi Y., Langdon A, Schoenbaum Geoffrey; Hippocampus is necessary for dopamine neurons to compute reward prediction errors when states are partially observable. Society for Neuroscience, 2022.

Tianming Y.*, Zhang, Z. & Cheng H. A recurrent network model for the neural mechanism of speed, accuracy and urgency signal in decision making. Computational and System Neuroscience, 2019.

Zhang Z, Cheng Z, & Yang T*. Modeling the Task State Representation by the Orbitofrontal Cortex with a Reservoir Network. Annual Meeting of the Institute of Neuroscience, 2016.