Xuexin Wei
- Associate Professor
- Neuroscience
Potentially Recruiting Students 26-27 Academic Year
Contact Information
Biography
Xue-Xin Wei grew up in Qingdao (or Tsingtao), China. After obtaining a Bachelor degree in mathematics and applied mathematics at Peking University, he became fascinated by the inner workings of the brain. In 2010 he moved to UPenn to pursue a Ph.D in psychology. He was the recipient of Louis B. Flexner Award, which gives to the Outstanding Thesis Work on any topics related to Neurosciences at UPenn. He spent three years as a postdoctoral research scientist at Center for Theoretical Neuroscience & Department of Statistics at Columbia University. In 2020, Xue-Xin started as a faculty member in Department of Neuroscience. In 2025, he was named a Sloan Research Fellow.
Dr. Wei can be reached at weixx@utexas.edu.
Research
The lab aims to understand how the brain supports adaptive and intelligent behavior. To tackle this fundamental question, we take a combination of bottom-up (analyzing experimental data and construct constrained mechanistic models ) and top-down (normative models) approach. Our research covers a variety of topics, including 1) using efficient computation as a guiding principle to understand cognition; 2) normative models of visual physiology and perception; 3) linking the geometry of neural representation to behavior; 4) generalization in the brain and artificial intelligence systems; 5) building probabilistic models of neural population responses. Much of the research is in the domains of vision and navigation.
The lab works at the intersection of Computational Neuroscience, Statistics, and AI/deep learning. We leverage the computational power of deep learning to help understand computations in the brain. We are also interested in using the prior knowledge and constraints obtained from the brain to develop more powerful AI systems. We work closely with experimentalists to develop sophisticated statistical techniques to understand how the neural responses are determined by the inputs and internal states on a trial-by-trial basis. These statistical models could provide specific constraints on the operations at the neural circuit level. As a further step, we can construct quantitative models constrained by these observations to reveal computational mechanisms underlying neural computation.
Dr. Wei can be reached at weixx@utexas.edu.
Research Areas
- Artificial Intelligence and/or Robotics
- AI for Health or Computational Science
- Statistics, Big Data or Machine Learning
- Neuroscience
Fields of Interest
- Computational
- Cognition/Sensory Systems
- Learning/Memory/Plasticity
- Computational/Theoretical
Centers and Institutes
- Center for Learning and Memory
- Center for Perceptual Systems
- Center for Theoretical and Computational Neuroscience
Education
- PhD, University of Pennsylvania
Publications
15. Hahn, M., Wang, E. & Wei, X.X. (2026). Identifiability of Bayesian models of perception. Proceedings of the National Academy of Sciences, in press.
14. Le, D. & Wei, X.X. (2026). Split-trial analysis reveals the information capacity of neural population codes. eLife.
13. Xiong, H. D., Li, J.A., Wilson, R.C., Lee, K.* & Wei, X.X.* (2026). Large language models reorganize representational geometry during in-context learning. Conference on Language Modeling (COLM).
12. Qian, Y., Geisler, W.S. & Wei, X.X. (2025). Quantifying the similarity of neural representations using decision variable correlation. In Advances in neural information processing systems (NeurIPS).
11. O'Shea, R.T., Nauhaus, I., Wei, X.X.*, Priebe, N. J.* (2025). Luminance invariant encoding in primary visual cortex. Cell Reports.
10. Wei, X.X., & Woodford, M. (2025). Representational geometry explains puzzling error distributions in behavioral tasks. Proceedings of the National Academy of Sciences.
9. Hahn, M., & Wei, X.X. (2024). A unifying theory explains seemingly contradictory biases in perceptual estimation. Nature Neuroscience.
8. Zhu, R., & Wei, X.X. (2023). Unsupervised approach to decomposing neural tuning variability. Nature Communications.
7. Ajabi, Z., Keinath, A.T., Wei, X.X., Brandon, M.P. (2023). Population dynamics of the thalamic head direction system during drift and reorientation. Nature.
6. Kriegeskorte, N.*, & Wei, X. X.* (2021). Neural tuning and representational geometry. Nature Reviews Neuroscience.
5. Zhou, D. & Wei, X. X. (2020) Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE. In Advances in neural information processing systems (NeurIPS).
4. Cueva, C. J., & Wei, X. X. (2018). Emergence of grid-like representations by training recurrent neural networks to perform spatial localization. International Conference on Learning Representations (ICLR).
3. Wei, X. X., & Stocker, A. A. (2017). Lawful relation between perceptual bias and discriminability. Proceedings of the National Academy of Sciences, 114(38), 10244-10249.
2. Wei, X. X., & Stocker, A. A. (2015). A Bayesian observer model constrained by efficient coding can explain ‘anti-Bayesian' percepts. Nature Neuroscience, 18(10), 1509.
1. Wei, X. X., Prentice, J., & Balasubramanian, V.B. (2015). A principle of economy predicts the functional architecture of grid cells. eLife, 4, e08362.
Awards
- Sloan Research Fellowship