Huajin Tang(唐华锦)
 Director of Neuromorphic Computing Research Center at the College of Computer Science of Sichuan University
 Associate Editor of IEEE Transactions on Neural Networks and Learning Systems
 Associate Editor of IEEE Transactions on Cognitive and Developmental Systems
 Associate Editor of Frontiers in Neuromorphic Engineering
 College of Computer Science, Sichuan University
 Email: htang@scu.edu.cn & huajin.tang@gmail.com
Brief Bio
Huajin Tang (M’01) received the B.Eng. degree from Zhejiang University, Hangzhou, China, in 1998, the M.Eng. degree from Shanghai Jiao Tong University, Shanghai, China, in 2001, and the Ph.D. degree in electrical and computer engineering from the National University of Singapore, Singapore, in 2005. He was a System Engineer with STMicroelectronics, Singapore, from 2004 to 2006, and then a PostDoctoral Fellow with the Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia, from 2006 to 2008. He is currently a Professor with the College of Computer Science, Sichuan University, Chengdu, China. He has authored one monograph (SpringerVerlag, 2007) and over 30 international journal papers. His current research interests include neuromorphic computing, neuromorphic cognitive systems, neurocognitive robots, and machine learning. Dr. Tang serves as an Associate Editor of the IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, Associate Editor of IEEE Transactions on Cognitive and Developmental Systems， and Editorial Board Member of Frontiers in Robotics and AI.
Academic Services
 Associate Editor of IEEE Transactions on Neural Networks and Learning Systems 2012now
 Associate Editor of IEEE Transactions on Cognitive and Developmental Systems 2016
 Editorial Board Member of Frontiers in Robotics and AI 2014now
 Program Chair of 2013 IEEE Cybernetics and Intelligent Systems (CISRAM 2013)
 Program Chair of 2015 IEEE Cybernetics and Intelligent Systems (CISRAM 2015)
 Chair of IEEE Computatonal Intelligence Society Multimedia Subcommittee, 20142015
 Chair of IEEE Computatonal Intelligence Society Summer School Subcommittee 20152016
Education
 8/20015/2004
 Ph.D. Electrical & Computer Engineering National University of Singapore
 9/19987/2001
 M.E. Mechanical and Power Engineering Shanghai Jiao Tong University, China
 9/19947/1998
 B.E. Power and Energy Engineering Zhejiang University, China
Professional Experience
 2014now

Professor
College of Computer Science, Sichuan University, Chengdu, China  4/20082014

Research Scientist
Institute for Infocomm Research, A*STAR, Singapore  2/20063/2008

Postdoctoral Research Fellow
Queensland Brain Institute, University of Queensland, Australia  5/20042/2006

System Engineer
STMicroelectronics, Singapore
Honors and awards
 1
 IEEE CIS Outstanding TNNLS Paper Award. (for the paper titled “Rapid Feedforward Computation by Temporal Encoding and Learning with Spiking Neurons”, 24(10):15391552, 2013.)
 2
 Role Model Award, Institute for Infocomm Research, A*STAR Singapore, 20102011. I was one of the only four receiving the institute's prestigious Role Model Award among 600 strong staffs.
 3
 Research Scholarship, National University of Singapore, 20012004.
 4
 Student Grant, IJCNN 2002, IEEE Neural Network Society.
 5
 SIEMENS Scholarship, Shanghai Jiao Tong University, China, 20002001.
Publications
BOOKS
 Huajin Tang, K.C. Tan and Z. Yi. Neural Networks: Computational Models and Applications, SpringerVerlag, 2007.
 Qiang Yu, Huajin Tang, Jun Hu and Kay Chen Tan. Neuromorphic Cognitive Systems: A Learning and Memory Centered Approach. Under preparation for Intelligent Systems Reference Library Series, Springer, 2015.
JOURNAL
 X. Peng, B. Zhao, R. Yan, H. Tang, and Z. Yi. Bag of Events: An Efficient and Online Probabilitybased Lowlevel Feature Extraction Method for AER Image Sensors. IEEE Trans. on Neural Networks and Learning Systems, accepted, 2016.
 X. Peng, Z. Yu, Z. Yi, and H. Tang. Constructing the L2Graph for Robust Subspace Learning and Subspace Clustering. IEEE Trans. on Cybernetics, accepted, 2016.
 J. Hu, H. Tang, K.C. Tan, and H. Li. How the Brain Formulates Memory: A SpatioTemporal Model. IEEE Computational Intelligence Magazine, vol. 11, no. 2, pp. 5668, 2016.
 H. Tang, W. Huang, A. Narayanamoorthy, and R. Yan. Cognitive Memory and Mapping in a Brainlike System for Robotic Navigation. Neural Networks, accepted, 2016.
 X. Peng, H. Tang, L. Zhang, and Z. Yi. A Unified Framework for Representationbased Subspace Clustering of Outofsample and Largescale Data. IEEE Trans. on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS.2015.2490080, 2016.
 X. Peng, R. Yan, B. Zhao, H. Tang, and Z. Yi. Fast Low Rank Representation based Spatial Pyramid Matching for Image Classification. KnowledgeBased Systems, vol. 90, pp. 1422, 2015.
 Q. Yu, R. Yan, H. Tang, K. C. Tan, and H. Li. A Spiking Neural Network System for Robust Sequence Recognition. IEEE Trans. on Neural Networks and Learning Systems, vol. 27, no. 3, pp. 621635, 2016.
 V. A. Shim, C. S. N. Ranjit, B. Tian, M. Yuan, and H. Tang. A Simplified Cerebellar Model with Prioritybased Delayed Eligibility Trace Learning for Motor Control. IEEE Trans. on Autonomous Mental Development, vol. 7, no. 1, pp. 2638, 2015. [preprint PDF]
 V. A. Shim, K. C. Tan, and H. Tang. Adaptive Memetic Computing for Evolutionary Multiobjective Optimization. IEEE Trans. on Cybernetics, in press, 2015. [preprint PDF]
 B. Zhao, R. Ding; S. Chen, B. LinaresBarranco, and H. Tang. Feedforward Categorization on AER Motion Events using Cortexlike Features in a Spiking Neural Network. IEEE Trans. on Neural Networks and Learning Systems, vol. 26, no. 9, pp.19631978, 2015. [preprint PDF]
 M. Yuan, H. Tang, and H. Li. RealTime Keypoint Recognition Using Restricted Boltzmann Machine. IEEE Trans. on Neural Networks and Learning Systems, vol. 25, no. 11, pp. 2119  2126, 2014. [PDF]
 H. Tang, K. Ramanathan, and N. Ning. Guest editorial: Special issue on brain inspired models of cognitive memory. Neurocomputing, vol. 138, pp. 12, 2014. [PDF]
 Q. Yu, H. Tang, K. C. Tan, and H. Yu. A braininspired spiking neural network model with temporal encoding and learning. Neurocomputing, vol. 138, pp. 312, 2014, [PDF]
 W. Huang, H. Tang, and B. Tian. Vision Enhanced NeuroCognitive Structure for Robotic Spatial Cognition. Neurocomputing, vol. 129, pp. 4958, 2014. [PDF]
 Q. Yu, H. Tang, K. C. Tan, and H. Li. PreciseSpikeDriven Synaptic Plasticity: Learning HeteroAssociation of Spatiotemporal Spike Patterns. PLoS One 8(11): e78318, 2013. [PDF][Code]

Q. Yu, H. Tang, K. C. Tan, and H. Li. Rapid Feedforward Computation by Temporal Encoding and Learning with Spiking Neurons. IEEE Trans. on Neural Networks and Learning Systems,vol. 24, no. 10, pp. 15391552, 2013. [PDF] [This paper wins "IEEE CIS Outstanding TNNLS Paper Award"]
PHYS.org Report: "Neural networks that function like the human visual cortex may help realize faster, more reliable pattern recognition", July 16, 2014.Read more at: http://phys.org/news/201407neuralnetworksfunctionhumanvisual.html#jCp  J. Hu, H. Tang, K.C. Tan, H. Li and L. Shi. A SpikeTiming Based Integrated Model for Pattern Recognition. Neural Computation, vol. 25, no. 2, pp. 450472, 2013. [PDF]
 J. Yu, H. Tang, and H. Li. Dynamics Analysis of A Population Decoding Model. IEEE Trans. on Neural Networks and Learning Systems, vol. 24, no. 3, pp. 498504, 2013. [PDF]
 J. Yu, H. Tang, H. Li, and L. Shi. Dynamical Properties of Continuous Attractor Neural Network with Background Tuning. Neurocomputing, vol. 99, pp. 439447, 2013. [PDF]
 E.Y. Cheu, J. Yu, C. H. Tan, and H. Tang. Synaptic Conditions for AutoAssociative Memory Storage and Pattern Completion in Jensen et al.'s Model of Hippocampal Area CA3. Journal of Computational Neuroscience, vol. 33, no. 3, pp. 435447, 2012. [PDF] ScienceDaily Report: "MemoryMaking Is All About the Connection", 8 Nov 2012.
 J. Yu, H. Tang, and H. Li. Continuous Attractors of DiscreteTime Recurrent Neural Networks. Neural Computing and Applications, DOI: 10.1007/s0052101209755, 2012.
 R. Yan, K. P. Tee, Y. W. Chua, H. Z. Li, and H. Tang. Gesture Recognition Based on Localist Attractor Networks with Application to Robot Control. IEEE Computational Intelligence Magazine, vol. 7, no. 1, pp. 6474, 2012. [PDF] ScienceDaily Report: "Robots Will Quickly Recognize and Respond to Human Gestures, With New Algorithms", 23 May 2012.
 H. Tang, H. Li. Book Review: Information Theoretic Learning: Renyi's Entropy and Kernel Perspectives. IEEE Computational Intelligence Magazine, vol. 6, no. 3, pp. 6062, 2011. PDF
 H. Tang, H. Li, and Z. Yi. Online learning and stimulusdriven responses of neurons in visual cortex. Cognitive Neurodynamics, vol. 5, no. 1, pp. 7785, 2011. PDF
 H. Tang, H. Li, and R. Yan. Memory dynamics in attractor networks with saliency weights.Neural Computation, vol. 22, no. 7, pp. 18991926, 2010. PDF
 H. Tang, H. Li and Z. Yi. A discretetime neural network for optimization problems with hybrid constraints. IEEE Trans. on Neural Networks, vol. 21, no. 7, pp. 11841189, 2010. PDF
 C. Giacomantonio, J. Hunt, H. Tang, D. Mortimer, S. Jaffer, V. Vorobyov, G. Ericksson, F. Sengpiel and G. J. Goodhill. Natural scene statistics and the structure of orientation maps in the visual cortex. NeuroImage, vol. 47, pp. 157172, 2009. PDF
 H. Tang, L. Weng, Z. Y. Dong and R. Yan. Adaptive and learning control for SI engine model with uncertainties. IEEE/ASME Trans. on Mechatronics, vol. 14, no. 1, pp. 93104, 2009. PDF
 H. Tang, L. Weng, Z. Y. Dong and R. Yan. Engine control design using globally linearizing control and sliding mode. Transactions of the Institute of Measurement and Control, vol. 32, no. 2, pp. 225247, 2010. PDF
 L. Zou, H. Tang, K. C. Tan and W. Zhang. Nontrivial global attractors in 2D multistable attractor neural networks. IEEE Trans. on Neural Networks, vol. 20, no. 11, pp. 18421851, 2009.PDF
 L. Zou, H. Tang, K. C. Tan and W. Zhang. Analysis of continuous attractors for 2D linear threshold neural networks. IEEE Trans. on Neural Networks, vol. 20, no. 1, pp. 175180, 2009.PDF
 E. J. Teoh, K. C. Tan, H. Tang,, C. Xiang and C. K. Goh. An asynchronous recurrent linear threshold network approach to solving the traveling salesman problem. NeuroComputing, vol. 71, issue 79, pp. 13591372, 2008. PDF
 H. Qu, Z. Yi and H. Tang. A columnar competitive model for solving multitraveling salesman problem. Chaos, Solitons and Fractals, vol. 31, no. 4, pp. 10091019, 2007.
 H. Qu, Z. Yi and H. Tang. Improving Local Minima of Columnar Competitive Model for TSPs.IEEE Trans. on Circuits and SystemsII, vol. 53, no. 6, pp. 13531362, 2006.
 H. Tang, K. C. Tan, and E. J. Teoh. Dynamics analysis and analog associative memory of networks with LT neurons. IEEE Trans. on Neural Networks, vol 17, no. 2, pp. 409418, 2006.PDF
 H. Tang, K. C. Tan and W. Zhang. Analysis of cyclic dynamics for networks of linear threshold neurons. Neural Computation, vol. 17, no. 1, pp. 97114, 2005. PDF
 K. C. Tan, H. Tang and S. S. Ge. On parameter settings of Hopfield networks applied to traveling salesman problems. IEEE Trans. on Circuits and Systems  I, vol. 52, no. 5, pp. 9941002, 2005. PDF
 K. C. Tan, H. Tang and W. Zhang. Qualitative analysis for recurrent neural networks with linear threshold transfer functions. IEEE Trans. on Circuits and SystemsI, vol. 52, no. 5, pp. 10031012, 2005. PDF
 H. Tang, K. C. Tan and Z. Yi. A columnar competitive model for solving combinatorial optimization problems. IEEE Trans. on Neural Networks, vol. 15, no. 6, pp. 15681573, 2004.PDF
 K. C. Tan, H. J. Tang and Z. Yi. Global exponential stability of discretetime neural networks for constrained quadratic optimization. NeuroComputing, vol. 56, pp. 399406, 2004. PDF
 K. C. Tan and H. J. Tang. New dynamical optimal learning for linear multilayer FNN. IEEE Trans. on Neural Networks, vol. 15, no. 6, pp. 15621568, 2004. PDF
 Z. Yi, Yan Fu and H. J. Tang. Neural networks based approach for computing eigenvectors and eigenvalues of symmetric matrix. Computers and Mathematics with Application, vol. 47, pp. 11551164, 2004.
CONFERENCE PUBLICATIONS/TALKS
 M. Yuan, B. Tian, V. A. Shim, H. Tang and H. Li. An EntorhinalHippocampal Model for Simultaneous Cognitive Map Building. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI15), Austin, TX, USA, 2015. (Acceptance rate: 26.67%) [Oral Presentation (rate 11.75%)] [Preprint PDF]
 X. Peng, Z. Yi and H. Tang. Robust Subspace Clustering via Thresholding Ridge Regression. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI15), Austin, TX, USA, 2015. (Acceptance rate: 26.67%) [Preprint PDF] [Codes & Data]
 V. A. Shim, B. Tian, M. Yuan, H. Tang, H. Li. DirectionDriven Navigation Using Cognitive Map for Mobile Robots. Proc. of 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Chicago, USA, 2014, pp. 26392646. [PDF]
 B. H. Tan, H. Tang, R. Yan, and J. Tani. A Flexible and Robust Robotic Arm Design and Skill Learning by Using Recurrent Neural Networks. Proc. of 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Chicago, USA, 2014, pp. 522529. [PDF]
 B. Zhao, Q. Yu, H. Yu, S. Chen, H. Tang. A Bioinspired Feedforward System for Categorization of AER Motion Events. Proc. of IEEE Biomedical Circuits and Systems Conference (BioCAS), Oct 2224, 2014, Lausanne, Switzerland, pp. 912.
 B. Tian, V. A. Shim, M. Yuan, C. Srinivasan, H. Tang, H. Li. RGBD Based Cognitive Map Building and Navigation. Proc. of 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Nov 38, Tokyo, Japan, 2013, pp. 15621567. [Preprint PDF][Supplementary Video]
 Vui Ann Shim, Chris Stephen Naveen Ranjit, Bo Tian, and H. Tang. A Simplified Cerebellumbased Model for Motor Control in Brain Based Devices. 20th International Conference on Neural Information Processing (ICONIP2013), Nov 37, Daegu, Korea, 2013. [Preprint PDF]
 C. H. Tan, H. Tang, E. Y. Cheu, and J. Hu. A Computationally Efficient Associative Memory Model of Hippocampus CA3 using Spiking Neurons. International Joint Conference on Neural Networks (IJCNN), Aug 49, Dallas, US, 2013.
 J. Hu, H. Tang, and K. C. Tan. A Hierarchical Organized Memory Model Using Spiking Neurons. International Joint Conference on Neural Networks (IJCNN), Aug 49, Dallas, US, 2013.
 H. Tang, B. Tian, Vui Ann Shim, and K. C. Tan. A NeuroCognitive System and Its Application in Robotics. Prof. of 10th IEEE International Conference on Control & Automation (ICCA), IEEE Press, pp. 406  411, June 1214, Hangzhou, China, 2013. [PDF]
 J. Dennis, Q. Yu, H. Tang. H. D. Tran, and H. Li. Temporal Coding of Local Spectrogram Features for Robust Sound Recognition, Proc. ICASSP 2013, IEEE, pp. 803807, May 2013.[PDF] ScienceDaily Report: "Audio Processing: Computers Following the Brain's Lead", 6 Nov 2013.(http://www.sciencedaily.com/releases/2013/11/131106084430.htm)
 J. Hu, H. Tang, and K. C. Tan. Spikingtiming based pattern recognition with realworld visual stimuli. Prof. of IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB), IEEE Press, pp. 2328, Singapore, April 1619, 2013.
 H. Tang, Q. Yu, and K. C. Tan. Learning RealWorld Stimuli by SingleSpike Coding and Tempotron Rule. IEEE World Congress on Computational Intelligence (WCCI), June 1015, Brisbane, Australia, 2012.
 Q. Yu, K. C. Tan, and H. Tang. Pattern recognition computation in a spiking neural network with temporal encoding and learning. IEEE World Congress on Computational Intelligence (WCCI), June 1015, Brisbane, Australia, 2012.
 H. Tang, W. Huang. Brain Inspired Cognitive System for Learning and Memory. 18th International Conference on Neural Information Processing (ICONIP), Nov 1417, Shanghai, China, 2011.
 W. Huang, H. Tang, J. Yu and C. H. Tan. A NeuroCognitive Robot for Spatial Navigation. 18th International Conference on Neural Information Processing (ICONIP), Nov 1417, Shanghai, China, 2011.
 C. H. Tan, E. Y. Cheu, J. Hu, Q. Yu and H. Tang. Associative Memory Model of Hippocampus CA3 Using Spike Response Neurons. 18th International Conference on Neural Information Processing (ICONIP), Nov 1417, Shanghai, China, 2011.
 H. Tang, V. A. Shim, K. C. Tan and J. Y. Chia. Restricted Boltzmann Machine Based Algorithm for Multiobjective Optimization. IEEE World Congress on Computational Intelligence (WCCI), July 1823, Barcelona , Spain , 2010.
 Huajin Tang, Haizhou Li and Zhang Yi. Stimulusdriven responses of neurons in visual cortex exhibit geometrical regularities. 7th International Symposium on Neural Networks (ISNN), June 69, Shanghai, China , 2010.
 Huajin Tang, C. H. Tan, K. C. Tan. Neural network versus behavior based approach in simulated car racing. IEEE Workshop on Evolving and SelfDeveloping Intelligent Systems, March 30April 2, Nashville, TN, USA, 2009.
 X. Xu, Huajin Tang, X. Shi. A fast algorithm for solving large scale nonlinear optimization problems using RNN. IEEE International conference on cybernetics and intelligent systems, Sep 2124, Chengdu, China , 2009.
 E. J. Teoh, Huajin Tang and K. C Tan. A Columnar Competitive Model with Simulated Annealing for Solving Combinatorial Optimization Problems. Proc. IEEE International Joint Conference on Neural Networks(IJCNN), Vancouver, BC, Canada, July 1621, pp. 32543259, 2006.
 R. Yan, M. J. Er, and Huajin Tang. An improvement on competitive neural networks applied to image segmentation. Advances in Neural NetworksISNN, Chengdu, China, 2006. (Also available in Lecture Notes in Computer Science, vol. 3972, pp. 498503, 2006).
 Huajin Tang, K. C. Tan and T. H. Lee. Dynamical optimal learning for FNN and its applications. FUZZIEEE, July 2529, Budapest, Hungary, 2004.
 Huajin Tang, K. C. Tan and T. H. Lee. Stability analysis of Hopfield neural networks for solving TSP. Proc. of the Second International Conference on Computational Intelligence, Robotics and Autonomous Systems (CIRAS), Singapore, 2003.
 Huajin Tang, K. C. Tan and T. H. Lee. Competitive neural networks for solving combinatorial optimization problems. Proc. of the Second International Conference on Computational Intelligence, Robotics and Autonomous Systems (CIRAS), Singapore, 2003.
 Huajin Tang, K. C. Tan and Z. Yi. Convergence analysis of discrete time recurrent neural networks for linear variational inequality. Proc. of IEEE International Joint Conference on Neural Networks (IJCNN), pp. 24702475, Honolulu, Hawaii , USA, 2002.
 PATENT:Shim Vui Ann, Tian Bo, Yuan Miaolong, Tang Huajin and Li Haizhou. "A Navigation System for Mobile Robots using DirectionDriven With Asymmetrical Multilayered Module", Singapore Filed Patent. No. 10201403296Y, filed 16Jun2014.