Tai Sing Lee, PhD

  • Adjunct Associate Professor, Neuroscience, Center for the Neural Basis of Cognition





Personal Website

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Education & Training

PhD, Harvard University (1993)


115 Mellon Institute

Research Interest Summary

Study of visual perception, perceptual organization, neural plasticity and neural coding; computer vision.

My research involves the application of computational, modeling and electrophysiological techniques to study the neural basis of visual perception and recognition. The current effort of my laboratory is focused on understanding how the brain actively constructs an internal representation of the perceptual world and how behaviors and experience transform the neural circuitry underlying visual processing. Specific issues include feedback and hierarchical computation, dynamic and attentive vision, plasticity and learning, neural coding and decoding. We also seek to integrate representations and algorithms underlying biological computation into development of new robotic vision system. My laboratory offers training opportunities in primate electrophysiology, computational modeling, statistical data analysis and computer vision.

Tang S, Zhang, Y, Li Z, Li M., Liu F., Jiang H, Lee, TS (2018) Large-scale two-photon imaging revealed super-sparse population codes in V1 superficial layer of awake monkeys eLife 2018.


Huang G. , Ramachandran S. Lee, T.S. and Olson C.R. (2018) Neural Correlate of Visual Familarity in Macaque Area V2 J. Neuroscience 2018. (* Lee as co-corresponding senior author)


Tang, S, Lee TS, Li M, Zhang, Y, Xu Y, Liu F, Teo B, Jiang H (2018) Complex pattern selectivity in Macaque primary visual cortex revaled by large-scale two-photon imaging Current Biology (28): 1: 38-48. (* Lee as co-corresponding senior author)


Samonds JM, Tyler, C, Lee TS. (2016) Evidence of Stereoscopic Surface Disambiguation in the Responses of V1 Neurons Cerebral Cortex In Press.

Zhang Y, Li X, Samonds JM, Lee TS. (2016) Relating functional connectivity in V1 neural circuits and 3D natural scenes using Boltzmann machines. Vision Research Special issue on Scene Statistics, in Press.

Lee, T.S. (2015) The Visual System's Internal Models of the World Proceedings of the IEEE Vol 103, issue 8, 1359-1378.

Li, X, Wang, B, Liu Y. T.S. Lee. (2015) Stochastic feature mapping for PAC-Bayes classifciation Machine Learning Vol 101, issue 1-3, 5-33.

Zhao, Mingmin, Zhuang Chengxu, Wang, Yizhou, Lee, T.S. (2014) Predictive encoding of contextual relationships for perceptual inference, interpolation and prediction International Conference on Learning Representation (workshop paper) archived (http://arxiv.org/abs/1411.3815) accepted.


Samonds, J.M., Potetz, B., Tyler, C., Lee, T.S. (2013) Recurrent connectivity can account for the dynamics of disparity processing in V1 Journal of Neuroscience, 33(7):2934 –2946.

Lee, T.S. (2015) The Visual System's Internal Models of the World Proceedings of the IEEE Vol 103, issue 8, 1359-1378.


Kelly, R.C., Smith, M.A., Kass, R.E., T.S. Lee (2010) Accounting for network effects in neuronal responses using L1 regularized point process models NIPS -- Advances in Neural Information Processing Systems, 23: 1099-1107.


Lee, T.S., Yang, C., Romero, R. and Mumford, D. Neural activity in early visual cortex reflects experience and higher order perceptual saliency. Nature Neuroscience, 5(6), 589-597, 2002.


Lee, T.S., Mumford, D. Hierarchical Bayesian inference in the visual cortex. Journal of Optical Society of America, A. 20 (7): 1434-1448, 2003.