Dr. Tai Sing Lee Professor in Computer Science Computer Science Department and Center for the Neural Basis of Cognition Carnegie Mellon University Pittsburgh, PA 15213 Office: Mellon Institute Rm 115 Phone: 412-268-1060 Email: tai at cnbc dot cmu dot edu

Faculty member in the Computer Science Department, Center for the Neural Basis of Cognition , and Machine Learning Department, Carnegie Mellon University, and adjunct faculty member of the Department of Neuroscience, University of Pittsburgh.

Director of Intercolledge Undergraduate Minor in Neural Computation, Carnegie Mellon University. Coordinator of Undergraduate Training fellowship in Computational Neuroscience (Program in Neural Computation).

Director of Tsinghua University - Carnegie Mellon University Summer Program in Computer Science, School of Computer Science

Research interests: computational neuroscience, computational vision, neurophysiology of the primate visual systems, active and adaptive vision, hierarchical coding and inference, mid-level vision, development of infant vision, learning and adaptation, structure of neural codes.

Courses and Seminars

• Artificial Intelligence (15-381/681) Fall 2017
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• Machine Learning from Neural Cortical Circuits (15-874) Spring 2016
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• Neural Computation (15-386/686) Spring 2019 Spring 2020 Spring 2021 Spring 2022 Spring 2023
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• Computer Vision (15-385/685) Spring 2011, Spring 2013.
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• Computational Perception (86-375/675) (15-387) Spring 2018, Fall 2019, Fall 2020 , Fall 2021 Fall 2022 , Fall 2023
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• Computational Neuroscience of Vision (86-712) Fall 2012
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• Advanced Topics in Computational Neuroscience (2002-2008) with Mike Lewicki
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• Lee Lab meeting Presentations
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Projects

Project MICrONS at Carnegie Mellon. Carnegie Mellon joins IARPA project to reverse engineering brain algorithms .

Education

• S.B. Engineering Physics, Harvard College (1986) .
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• Ph.D. Engineering Sciences, Harvard University (1993) .
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• Ph.D. Medical Physics and Engineering, Harvard-MIT Division of Health Science and Technology (1993)
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• Postdoc. Biomedical Physics and Neurophysiology, Harvard University (DAS) and Massachusetts Institute of Technology (BCS) (1993-96) .
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• Society of Harvard Engineers and Scientists Honor Scholar (1985) .
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• Harvard-MIT HST Fellowship Award (1986) .
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• McDonnell Pew Foundation Young Investigator Research Award (1996) .
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• NSF Faculty Career Development (CAREER) Award (2000).
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• ICCV Helmholtz prize (2013) .
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Current Postdoctoral Fellows

Current Ph.D. and M.S. Students

• Ge (Summer) Huang Neural Computation, Carengie Mellon.
• Tianqin Li Computer Science, Carengie Mellon.
• Junru Zhao Biomedical Engineering, Carengie Mellon.
• Dunhan Jiang Computational Biology, Carengie Mellon.
• Leiran Song Electrical Engineering, Carengie Mellon.
• Hadrien Helfgott Computer Science, Carengie Mellon.
• Jingtao Zhang Computer Science, Carengie Mellon.

Current Undergraduate Research Fellows

• Issac Lin , Computer Science, Carnegie Mellon, CMU
• Alan Ramirez , Computer Science, Carnegie Mellon, CMU

Former Ph.D. and M.S. Students

• Stella Yu , Ph.D. Robotics 2002, CMU. Assistant professor in computer science, Boston college, MA.
• Brian Potetz , Ph.D. Computer Science, 2008, CMU. Assistant professor in computer science, University of Kansas. Google Research CA.
• Ryan Kelly , Ph.D. Computer Science, 2010, CMU. Google Research New York.
• Tom Stepleton , Ph.D. Robotics 2010, CMU. Deep Mind, London.
• Xiong Li , Ph.D. Automation 2013, Shanghai JiaoTong. Start-up. China
• Yimeng Zhang , Ph.D. Computer Science, 2021. Carnegie Mellon. Pinterest.
• Andrew Luo Neural Computation, 2021. Ph.D. program, Carengie Mellon.
• Liyuan Liang Neural Computation, 2021. Ph.D. program, Carengie Mellon.
• Richard Romero , M.S. Computer Science. 1999. Co-Founder, Eizel Technology
• Ryan Poplin, M.S. in Neural Computation. 2009. Broad Institute, Boston. Google Research
• Gaya Mohankumar , M.S. 2017. in Biomedical engineering, Boston.
• Esha Uboweja , M.S. 2015. in Computer Science and Computer Vision, Amazon, Google Research.
• Shefali Umrania , M.S. 2017. in Computational Biology, Boston.
• Jingyuan Li , M.S. in Biomedical engineering, 2019. Ph.D. program in Biomedical Engineering, University of Washington.
• Yuanyuan Wei , M.S. in EECS, Ph.D. program in Biomedical Engineering, 2019. SUSTech- University of British Columbia.
• Mert Inan M.S. Computational Biology, 2019. Carengie Mellon. Ph.D. program in University of Pittsburgh
• Jiaxin Wang M.S. Computaitonal Biology, 2021. Carengie Mellon.
• Yusen Zhu M.S. Biomedical Engineering, 2021. Carengie Mellon. Ph.D. program at George Tech
• Xiaoqi Zhang M.S. Computaitonal Biology, 2021. Carengie Mellon. Ph.D. program at Purdue
• Shang (Gale) Gao Biomedical Engineering, Carengie Mellon. MIT Brain and Cognitive Science, Research Intern.
• Sicheng Dai Biomedical Engineering, Carengie Mellon. Ph.D Program Chinese Academy of Sciences.
• Mirudhula Mukundan Comutational Biology, Carengie Mellon. Biotech
• Zitong Wang Biomedical Engineering, Carengie Mellon. Ph.D. Program University of Pittsburgh.
• Yung Ying Chen Biomedical Engineering, Carengie Mellon. Ph.D. Program at Biomedical Engineering, GeorgiaTech
• Ziqi Wen Biomedical Engineering, Carengie Mellon. Ph.D. Program Cognitive Science/Brain Computer Interface at UC Santa Barbara
• Weifan Wang Biomedical Engineering, Carengie Mellon. Ph.D. program in Biomedical engineering. Carnegie Mellon

Former Postdoctoral Fellows

• Wenhao Zhang Ph.D. Biophysics (2016) Institute of Neuroscience, China. Asistant Professor, University of Texas SW Medical Center, Dallas.
• Jason Samonds , Ph.D. Electrical Engineering (2004) Vanderbilt University. Currently in U. Texas, Austin.
• Corentin Massot , Ph.D. Congitive Science, University of Glasgow & McGill University. Research assistant professor in U. Pittsburgh.
• Matthew Smith , Ph.D. Neuroscience (2002), New York University. Assistant Professor in Ophthalmology, University of Pittsburgh. Associate Professor in Biomedical Engineering, Carnegie Mellon.
• Xiaogang Yan , Ph.D. Biomedical Engineering (1990), Zhejiang University, PRC. York University.
• Yugou Yu , Ph.D. Physics (2001) Nanjing University, PRC. Professor in Computational Neuroscience, Fudan University, Shanghai, China.

Former Undergraduate Research Fellows (and their next steps)

• Zixin Sha , Computational Neuroscience, Carnegie Mellon, CMU. MD. Program, University of Florida.
• Wentao Qiu, YuanPi College, Computer Science, Peking University. Ph.D. program in Neuroscience at Northwestern.
• Danial Wang , Computer Science, CMU, Ph.D. program in Computer Science at Yale.
• Nicholas McNeal Computer Science, Ohio State University. Ph.D. program in Computer Science, Georgia Tech
• Hal Rockwell, Computational Biology, CMU. Ph.D. program in Neuroscience, University of Chicago
• Deying Song , Yuanpei College, Mathematics, Peking Univeristy . Ph.D. program in Neural Computation, Carnegie Mellon
• Aniekan Umoren , BCS and EECS, MIT. Ph.D. Program in Computational Neuroscience, Columbia University.
• Chen Guo , Computer Science, Tsinghua University, Ph.D. program, Tsinghua University
• Jennifer Huang , Computer Science, CMU. Facebook
• Simin Li , Computer Science, CMU. Amazon
• Yuqi Zhang , Computer Science, CMU. Apple
• Ho-Yin Chau, Physics, Mathematics, Computer Science, UC Berkeley , Ph.D. program in Theoretical Neuroscience, Columbia University.
• Julie Xueyan Niu , Mathematics, Hong Kong University, Ph.d. program in Theoretical Neuroscience, New York University
• Zinui Wu , Mathematics, Statistics and Machine Learning, Carnegie Mellon University, Master in Machine Leearning, Oxford University. Ph.D. program in EECS, MIT.
• Yue Xu , Computer Science, Carnegie Mellon University, CNS Ph.D. program at Caltech.
• Siming Yan , Computer Science, Peking University, Ph.D. program in Computer Science. UT Austin.
• Jielin Qiu , EE Shanghai Jiaotung University, Ph.D program in CS, Carnegie Mellon.
• Bowen Xiao , Computer Science, Peking University.
• Xuyang Fang , Computer Science, Peking University, MS program in Computer Science, Carnegie mellon.
• Hao Wang , Computer Science, Peking University. M.S. Program in CS, Stanford University.
• Xingyu Lin , Computer Science, Peking University. Ph.D. Program in CS, CMU.
• Tiancheng Zhi , Computer Science, Peking University. Ph.D. Program in CS, CMU.
• Elizabeth Ottens , Electrical and Computer Engineering, CMU, Apple Machine Learning Core Engineer
• Lingzhang Jiang , Computer Science, CMU, Singapore
• Dan Howarth , Computer Science, CMU, IARPA project, CMU.
• Mingmin Zhao , Computer Science, Peking University. Ph.D. Program in EECS, MIT.
• Chengxu Zhuang , Electronics Engineering, Tsinghua University, Ph.D. Program in computational neuroscience, Stanford University.
• Elizabeth Ottens , Electrical and Computer Engineering, CMU. Apple Computer.
• Lingzhang Jiang , Computer Science, CMU. Singapore.
• Dan Howarth , Computer Science, CMU, Currently, researcher at Carnegie Mellon.
• Shashank Singh , Computer Science, CMU. Ph.D. Program in machine learning, Carnegie Mellon University.
• Ben Poole , Computer Science, CMU, Computer Science Ph.D. program, Stanford University
• Grace Lindsay , Neuroscience, University of Pittsburgh. Fellow in Bernstein Center for Computational Neuroscience, Freiburg. Ph.D. program in Computational Neuroscience, Columbia University.
• Amber Xu , Electrical Engineering, CMU
• Andrew Noh , Electrical Engineering, CMU. Google.
• Ian Lenz , ECE, CIT CMU. Ph.D. program in EECS, Cornell University.
• Carl Doersch , Computer Science, CMU. Machine Learning Department Ph.D. program, CMU.
• Andrew Maas , Computer Science, CMU. Stanford Ph.D. program in Computer Science
• Ankit Khambhati , ECE, CMU. Ph.D. program in Biomedical Engineering, UPenn.
• Lei Liu , Neuroscience. M.D. Program Temple Medical School
• My Nguyen , Biology. Master, Heinz School of Public Policy, Carnegie Mellon
• Ken Shan , Mathematics. Ph.D. in Computer Science, Harvard University, assistant professor in computer science and cognitive science, Rutgers, State U. of New Jersey.
• Matt Easterday , Computer Science/Philosophy. Ph.d. in Human Computer Interaction, Carnegie Mellon University. Now Assistant Professor, Northwestern University.
• Mary Berna , Mechanical Engineering. Ph.D. program in Robotics, Carnegie Mellon.
• Cindy Yang , Biology. Ph.D. program in Neuroscience, UCSF.
• Scott Marmer , Electrical Engineering. J.D. Harvard Law School
• Alexandria Marino , Psychology. M.D./Ph.D. program, Yale Medical School
• Tom DuBois , Computer Science.
• Elise Cassidente , Computer Science. Law School
• Khary Mendez , Computer Science. Software Engineer.
• Iain Proctor , Computer Science.

Publications

• Lin, Isaac, Wang, T, Gao S., Tang, SM, Lee, TS (2024) Incremental Learning and self-attention mechanism improve neural system identification. Submitted to NeuRIPS.
• Wang, WF, Niu X., Lee, TS (2024) Manifold Transform by Recurrent Cortical Circuit Enhance Robust Encoding of Familiar Stimuli. Submitted to NeurIPS.
• Wang T., TS Lee,* Yao Hao*, Hong J, Li Y, Jiang H, IM, Andolina, S.M Tang (2024) Large-scale calcium imaging reveals a systematic V4 map for encoding natural scenes. * co-first author. Nature Communication 15. 6410 (2024).
• Wen, Ziqi, Li, Tianqin, Jing, Zhi, and T. S. Lee (2024) Does resistance to style-transfer equal global shape bias? Measuring network sensitivity to global shape configuration. ICLR 2024 Workshop on Representational Alignment. International Conference in Learning Representation.
• Zhao, J., Li,Tianji, and Lee, T.S. (2024) The benefits of Incorporating Shape Priors in Contrastive Learning ICLR 2024 Workshop on Representational Alignment. International Conference in Learning Representation.
• Li, T, Wen, Z., Li, Y., Lee, TS (2023) Emergence of Shape Bias in Convolutional Neural Networks through Activation Sparsity NeuRIPS 2024 (Thirty-seventh Conference on Neural Information Processing Systems) (Oral Presentation).
• Wang T., Yao Hao*, Lee, TS*, Hong J, Li Y, Jiang H, IM, Andolina, S.M Tang (2023) A calcium imaging large dataset reveals novel functional organization in macaque V4. * co-first author. arXiv. 2307.00932
• Massot, C., Zhang, X. *, Wang, ZT.*, Rockwell, H., Panadreous G., Yuille, A., TS Lee. (2023) Cue-invariant Coding of Edge Stimuli in Macaque Early Visual Cortex * co-first author. https://www.biorxiv.org/content/10.1101/2023.12.05.570110v1
• Rockwell, H, Dai, S*, Zhang, YM, Tsou S., Huang G., Wei YY, T.S. Lee (2023) Recurrent Circuits Improve Neural Response Prediction and provide Insight into Cortical Circuits. Computational and Systems Neuroscience (Cosyne) 2023.
• Gao S., Wang T.*, Jue X., Wang, D., Lee, TS#, S.M. Tang (2023) A large dataset of Macaque V1 Responses to Natural Images Revealed Complexity in V1 Neural Codes * Co-First Author. #co-corresponding senior author. Computational and Systems Neuroscience (Cosyne) 2023.
• McNeal, N., Huang, J., Umoren, A, Dai S., Dannenberg,R. Randall, R., TS Lee. (2022) Relating Human Perception of Musicality to Prediction in a Predictive Coding Model arXiv 2210.16587
• Li, T, Li, Z, Luo, A., Rockwell, H., Farimani, AB, TS Lee (2022) Prototype memory and attention mechanism for few shot image generation. International Conference On Learning Representations. .
• Zhang, Y., Rockwell, H. Huang, G., Tsou, S. Wei, YY, Lee. TS (2021) Recurrent circuits as multi-path ensembles for modeling responses of early visual cortical neurons arXiv. cs arXiv:2110.00825 . https://arxiv.org/abs/2110.00825
• Wu, Z, Rockwell, H, Zhang, Y, Tang, SM, Lee, T.S. (2021) Complexity and diversity in sparse code priors improve receptive field characterization of Macaque V1 neurons PLOS Computational Biology. October 2021. https://doi.org/10.1371/journal.pcbi.1009528
• Luo, A., Li, T., Zhang, WH, Lee, TS (2021) SurfGen: Adversarial 3D Shape Synthesis with Explicit Surface Discriminators Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 16238-16248.
• Kowalewski N., Kauttonen J., Stan PL, Chase S., Lee TS, and Sandra Kuhlman (2021) Development of Natural Scene Representation in Primary Visual Cortex Requires Early Postnatal Experience Current Biology vol 31 (2) p369-380, Jan 25, 2021
• Rockwell, H., Zhang YM, Mohankumar, G., Tsou S., Lee, TS (2020) Recurrent networks fitting neural temporal responses to natural images exhibit contextual modulation Computational and Systems Neuroscience (Cosyne) 2021.
• Massot Corentin, Zhang X., Rockwell, H., Papandreou G., Yuille, A., Lee TS (2020) Coding of pattern complexity in V1 and V2 neurons Computational and Systems Neuroscience (Cosyne) 2021.
• Niu, XY, Chau, HY, Lee, T.S., Zhang,WH (2020) Emergence of opposite neurons in decentralized firing rate model of multisensory processing In preparation for PLOS Computational Biology. also bioRxiv at https://www.biorxiv.org/content/10.1101/845743v3.full.
• Zhang,WH, Lee TS, Doiron, B, Si Wu (2020) Distributed sampling-based Bayesian inference in Coupled Neural Circuit Computational and Systems Neuroscience (Cosyne) 2021, also bioRxiv at doi: https://doi.org/10.1101/2020.07.20.212126.
• Wu, Ziniu, Rockwell, Harold, Zhang, YM, Tang, SM, Lee TS (2020) Complex Sparse Code Priors Improve Receptive Field Models of Macaque V1 Neurons Submitted to PLOS Computational Biology. Also arXiv.1911.08241
• Chau, HY, Niu, XY, Zhang,WH, Lee TS (2020) Emergence of opposite neurons in a circuit for multisensory processing Computational and Systems Neuroscience (Cosyne) 2020. Also BioRxvi: doi: https://doi.org/10.1101/814483
• Zhang, W-H, Si, W, Doiron B, T.S. Lee (2019) A Normative Theory for Causal Inference and Bayes Factor Computation in Neural Circuits Conference on Neural Information Processing Systems (NeurIPS 2019).
• Wu, Z.N., Zhang,Y.M., Tang, S.M., Lee, TS (2019) Characterizing V1 neurons using convolutional neural networks and project pursuit models with interpretable sparse coding kernels Computational and Systems Neuroscience (Cosyne) 2019.
• Zhang, W-H, Wang H, Chen A, Gu Y, Lee, T.S., Wang, YYM, Wu Si (2019) Complementary congruent and opposite neurons achieve concurrent multisensory integration and segregation eLife eLife 2019;8:e43753
• 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, 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. (* Lee as co-corresponding senior author)
• Zhang, Y, Lee, T.S Li, M, Liu F, Tang, S (2018) Convolutional neural network modeling of V1 responses to complex patterns Journal of Computational Neuroscience 2018.
• 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, J., Feese, BD, Lee TS, S. Kuhlman (2017) Nonuniform surround suppression of visual responses in mouse V1 J. Neurophysiology 118: 3282-3292
• Li, M, Liu F, Jiang H, Lee TS, Tang, S (2017) Long-Term Two-Photon Imaging in Awake Macaque Monkey Neuron 93(5):1049-1057.
• Lin, X, Wang H, Li, Z, Zhang, Y, Yuille, A., Lee, T.S. (2017) Transfer of view-manifold learning to similarity perception of novel objects International Conference of Representatioanl Learning (ICLR) (conference paper) archived (https://openreview.net/pdf?id=B1gtu5ilg)
• Wang, H., Lin, XY, Zhang, Y, Lee, T.S. (2017) Learning Robustbat Object Recognition Using Composed Scenes from Generative Models 14th Conference of Computer Vision and Robotics. https://arxiv.org/abs/1705.07594
• Ya, Heqing, Sun, Haonan, Helt, Jeffrey, Lee, T.S.(2017) Learning to Associate Words and Images Using a Large-scale Graph 14th Conference of Computer Vision and Robotics 2017 https://arxiv.org/abs/1705.07768
• Samonds JM, Tyler, C, Lee TS. (2016) Evidence of Stereoscopic Surface Disambiguation in the Responses of V1 Neurons Cerebral Cortex (2017) 27 (3): 2260-2275.
• 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) 120: 121-131.
• 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)
• Zhang Y, Li X, Samonds JM, Poole B, Lee TS. Relating functional connectivity in V1 neural circuits and 3D natural scenes using Boltzmann machines. 2014. Proceedings of Computational and System Neuroscience, COSYNE 2015 (also under review in special issue of Vision Research on Scene Statistics).
• Li, X, Samonds J.M., Liu, Y, Lee, T.S. (2014) Encoding of 3D surface priors in the functional interactions among V1 disparity-tuned neurons. PNAS under review
• Samonds J.M., Tyler, C. Lee, T.S. (2014) Evidence of stereoscopic surface disambiguation in the responses of V1 neurons Cerebral Cortex under review.
• Samonds J.M., Potetz, B.R., Lee, T.S. (2014) Sample Skewness as a Statistical Measurement of Neuronal Tuning Sharpness Neural Computation 26(5): 860-906.
• Li, X., Wang B., Liu, Y., and Lee, T.S. (2013) Learning Discriminative Sufficient Statistics Score Space European Conference of Machine Learning (ECML),
• Samonds JM, Potetz, B, Tyler, C. Lee TS. (2013) Evidence of stereoscopic surface disambiguation and interpolation in the responses of V1 neurons. Proceedings of Computational and System Neuroscience, COSYNE 2014
• 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.
• Yan, XG, Khambhati, A., Liu, L., Lee, T.S. (2012) Neural dynamics of image representation in the primary visual cortex. Journal of Physiology, vol 106, 5-6: 250-265.
• Samonds, J.M., Potetz, B., Lee, T.S. (2012) Relative luminance and binocular disparity preferences are correlated in macaque primary visual cortex, matching natural scene statistics. Proceedings of the National Academy of Sciences (PNAS), 109 (16): 6313-6318.
• Samonds, J.M., Lee, T.S. (2011) Neuronal interactions and their role in solving the stereo correspondence problem. In Vision in 3D Environments, Ed. Laurence Harris, Michael Jenkin, Cambridge University Press.
• Li, X., Lee, T.S., Liu, Y. (2011) Hybrid Generative-Discriminative Classification using Posterior Divergence IEEE Conference in computer vision and pattern recognition (CVPR). 2713-2720.
• 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. .
• Kelly, R.C., Smith, M.A., Kass, R.E., T.S. Lee (2010) Local field potentials indicate network state and account for neuronal response variability. J. Computational Neuroscience. 29:567-579. .
• Potetz, B., Lee, T.S. (2010) Scene statistics and 3D surface perception. In Computational Vision: From Surfaces to Objects. Chapman Hall. Ed. C. W. Tyler. Chapman & Hall/CRC, chapt 1, pp. 1-25, (2010).
• Samonds, J.M., Potetz, B., Lee, T.S., (2009) Cooperative and competitive interactions facilitate stereo computations in macaque primary visual cortex J. Neuroscience 29(50):15780-15795, 2009. .
• Stepleton, T., Ghahramani, Z., Gordon G., Lee, T.S. (2009) The Block Diagonal Infinite Hidden Markov Model Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics. AND Journal of Machine Learning Research: 5: 544-551.
• Lee, T.S. (2009) Computational approaches in visual perception Encyclopedia of Perception, Ed. E.B. Goldstein et al., SAGE Press.
• Lee, T.S., Stepleton, T, Potetz, B and Samonds J. (2008) Neural coding of scene statistics for surface and object inference In Object Categorization: perspectives from human and machine vision, Ed. Sven Dickinson, Ales Leonardis, Bernt Schiele, Michael Tarr, Cambridge University Press.
• Lee, T.S. (2008) Contextual influences in visual processing. Encyclopedia of Neuroscience, Ed. M.D. Binder, N. Hirokawa and U. Windhorst, Springer-Verlag. in Press.
• Potetz, B. and Lee, T.S. (2008) Efficient belief propagation for higher order cliques using linear constraint nodes. Computer Vision and Image Understanding. 112(1): 39-54.
• Smith, M.A., Kelly, R.C., Lee, T.S., (2007) Dynamics of response to perceptual pop-out stimuli in macaque V1. J. Neurophysiology 98: 3436-3449.
• Kelly, R.C., Smith, M.A., Samonds, J.M., Kohn, A., Bonds, A.B., Movshon, J.A., Lee, T.S., (2007) Comparison of recordings from microelectrode arrays and single electrodes in visual cortex. J. Neuroscience 27: 261-264. .
• Samonds, J., Potetz, B., Lee, T.S., (2007) Neurophysiological evidence of cooperative mechanisms for stereo computation. NIPS -- Advances in Neural Information Processing Systems 19, 1201-1208, MIT Press.
• Lee, T.S., Yuille, A (2006) Efficient coding of visual scenes by grouping and segmentation: theoretical predictions and biological relevance. in Bayesian Brain, probabilistic approaches to neural coding. Ed. K. Doya, S. Ishii, R. Rao, A. Pougeti. MIT Press, 141-185.
• Potetz, B., Lee, T.S. (2006) Scaling Laws in Natural Scenes and the Inference of 3D Shape. NIPS -- Advances in Neural Information Processing Systems 18, 1089-1096, MIT Press .
• Yu, Y., Romero, R., Lee, T.S. (2005) Preference of sensory neural coding for 1/f signals. Physics Review Letters, 94 , 108103, 1-4.
• Yu, Y., Lee, T.S. (2005) Adaptive contrast gain control and information maximization Neurocomputing, 65-66(2005): 111-116.
• Yu, Y., Potetz, B., Lee, T.S. (2005) The role of spiking nonlinearity in contrast gain control and information transmission. Vision Research, 45(2005): 583-592.
• Stepleton, T., Lee, TS (2005) Using Co-occurrence and Segmentation to Learn Feature-based Object Models from Video. WACV/MOTION 2005 , 129-134
• Deco, D., Lee, T.S. (2004) The role of early visual cortex in visual integration: a neural model of recurrent interaction. European Journal of Neuroscience 20: 1089-1100.
• Kelly, R. and Lee, T.S. (2004) Decoding V1 Neuronal Activity using Particle Filtering with Volterra Kernels. Advances in Neural Information Processing Systems 15, MIT Press. . Ed. Thurn, S., Lawrence, KS, Bernhard, S. 1359-1366.
• Kelly, R. and Lee, T.S. (2004) Decoding visual input based on V1 neuronal activities with particle filtering. Neurocomputing. . 58-60: 849-855.
• Yu, Y. and Lee, T.S. (2004) Nonlinear dynamics of spike generation account for contrast adaptation. Proceedings of the Computational Neuroscience conference. . Spain.
• Yu, Y., Liu, F., Wang W., Lee, T.S. (2004) Optimal synchrony state for maximum information transmission. NeuroReport 15(10): 1605-1610.
• Lee, T.S., Mumford, D. (2003) Hierarchical Bayesian inference in the visual cortex. Journal of Optical Society of America, A. . 20(7): 1434-1448.
• Yu, Y., Lee, T.S. (2003) Dynamical mechanisms underlying contrast gain control in single neurons. Physics Review, E.. 68(1): 1901-1907.
• Lee, T.S. (2003) Neural basis of attentive perceptual organization. . In Perceptual Organization in Vision: Behavioral and Neural Perspectives Ed. M. Behrmann, C. Olson and R. Kimchi, Lawrence Erlbaum Associates, 431-457.
• Lee, T.S. (2003) Analysis and synthesis of visual images in the brain: evidence for Pattern theory. In Mathematical methods in computer vision, Lecture notes in Mathematics and its Application. Ed. P. Olver and A. Tannenbaum. Springer-Verlag, 87-106.
• Potetz, B., Lee, T.S. (2003) Statistical correlations between 2D images and 3D structures in natural scenes. Journal of Optical Socity of America, A. . 20(7): 1292-1303.
• Lee, T.S. (2003) Computations in the early visual cortex. J. Physiology (Paris) , 97(203), 121-139.
• Romero, R.D., Yu, Y., Afshar, P., Lee, T.S. (2003) Adaptation of the temporal receptive fields of Macaque V1 neurons Neurocomputing (52-54): 135-140.
• Yu, Y., Lee, T.S. (2003) Adaptation of the transfer function of the Hodgkin-Huxley (HH) neuronal model. Neurocomputing (52-54): 441-445.
• Lee, T.S., Yang, C., Romero, R.D., and Mumford, D. (2002) Neural activity in early visual cortex reflects behavioral experience and higher order perceptual saliency. Nature Neuroscience 5(6) . 589-597.
• Lee, T.S. (2002) The nature of illusory contour computation. Neuron 33(5) 667-668.
• Lee, T.S. (2002) Top-down influence in early visual processing: A Bayesian perspective. Behaviors and Physiology 77(4-5): 645-650.
• Romero, R., Lee, T.S. (2002). Spike train analysis for single trial data using Hidden Markov Model Neurocomputing 44-46: 597-604, Elsevier Press.
• Deco, G., Lee, T.S. (2002). An unified model of spatial and object attention based on inter-cortical biased competition. Neurocomputing 44-46: 769-774, Elsevier Press
• Yu, S., T.S. Lee, T. Kanade (2002) A hierarchical Markov Random Field Model for figure-ground Segregation Lecture Notes in Computer Science, 2134, pp 118-133, Springer-Verlag.
• Lee, T.S., Nguyen, M. (2001). Dynamics of subjective contour formation in early visual cortex. Proceedings of the National Academy of Sciences, U.S.A. , 98(4) 1907-1911.
• Romero, R.D., Lee T.S. (2001) Estimation of temporal kernels for cells in V1. Proceedings of Annual Conference in Computational Neuroscience .
• Lee, T.S., Yu, S. (2000) An information-theoretic framework for understanding saccadic behaviors. Advances in Neural Information Processing Systems 12: 834-840 , Ed. S.A. Solla, T.K. Leen, K-R. Muller. MIT Press.
• Yu, S., Lee T.S. (2000) What V1 neurons tell us about saccadic suppression. Neurocomputing Elsevier Press, 32-33, 271-277.
• Cassidente, E., Yan X.G, Lee T.S. (2000) A Bayesian decision approach to evaluate local and contextual information from spike trains. Neurocomputing , 32-33, 1013-1020 Elsevier Press.
• Yan, X.G. and Lee, T.S. (2000). Informatics of spike trains in neuronal ensemble. Proceedings of the International Conference of Biomedical Engineering . 5978-655226, 1-6.
• Lee, T.S., D. Mumford, R. Romero and V.A.F. Lamme (1998). The role of primary visual cortex in higher level vision. Vision Research 38, 2429-2454.
• Lee, T.S., D. Mumford, S.C. Zhu and V.A.F. Lamme (1997). The role of V1 in shape representation. Proceedings of the Annual Conference of Computational Neuroscience 96 .
• Lee, T.S. (1996). Image representation using 2D Gabor wavelets. IEEE Transection of Pattern Analysis and Machine Intelligence. Vol. 18, No. 10, October, 959-971.
• Lee, T.S. (1995). Neurophysiological evidence for image segmentation and medial axis computation in primate V1. Computational Neuroscience . (Ed. Jim Bower). Academic Press. 373-378.
• Lee, T.S. (1995). A Bayesian framework for understanding texture segmentation in the primary visual cortex. Vision Research 35, 2643-2657..
• Zhu, S.C., Lee, T.S., and Yuille, A. (1995). Region competition: unifying snakes, region growing and MDL for image segmentation. Proceedings of the Fifth International Conference in Computer Vision 416-425.
• Lee, T.S. (1994). Representational strategy in the visual cortex. Proceedings of First International Conference in Image Processing, Texas,1994. 2: 590-595.
• Lee, T.S., D. Mumford, A. Yuille (1992). Texture segmentation by minimizing vector-valued energy functionals: the coupled-membrane model. Lecture Notes in Computer Science 588, 165-173, Springer-Verlag.