Publications

BOOKS

  1. Huajin Tang, K.C. Tan and Z. Yi. Neural Networks: Computational Models and Applications, Springer-Verlag, 2007.
  2. 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

  1. X. Peng, B. Zhao, R. Yan, H. Tang, and Z. Yi.  Bag of Events: An Efficient and Online Probability-based Low-level Feature Extraction Method for AER Image Sensors IEEE Trans. on Neural Networks and Learning Systems, accepted, 2016.
  2. X. Peng, Z. Yu, Z. Yi, and H. Tang.  Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering IEEE Trans. on Cybernetics, accepted, 2016.
  3. J. Hu, H. Tang, K.C. Tan, and H. Li.  How the Brain Formulates Memory: A Spatio-Temporal Model IEEE Computational Intelligence Magazine, vol. 11, no. 2, pp. 56-68, 2016.
  4. H. Tang, W. Huang, A. Narayanamoorthy, and R. Yan.  Cognitive Memory and Mapping in a Brain-like System for Robotic Navigation Neural Networks, accepted, 2016.
  5. X. Peng, H. Tang, L. Zhang, and Z. Yi.  A Unified Framework for Representation-based Subspace Clustering of Out-of-sample and Large-scale DataIEEE Trans. on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS.2015.2490080, 2016.
  6. X. Peng, R. Yan, B. Zhao, H. Tang, and Z. Yi.  Fast Low Rank Representation based Spatial Pyramid Matching for Image ClassificationKnowledge-Based Systems, vol. 90, pp. 14-22, 2015.
  7. Q. Yu, R. Yan, H. Tang, K. C. Tan, and H. Li.  A Spiking Neural Network System for Robust Sequence RecognitionIEEE Trans. on Neural Networks and Learning Systems, vol. 27, no. 3, pp. 621-635, 2016.
  8. V. A. Shim, C. S. N. Ranjit, B. Tian, M. Yuan, and H. Tang. A Simplified Cerebellar Model with Priority-based Delayed Eligibility Trace Learning for Motor ControlIEEE Trans. on Autonomous Mental Development, vol. 7, no. 1, pp. 26-38, 2015. [pre-print PDF]
  9. V. A. Shim, K. C. Tan, and H. Tang. Adaptive Memetic Computing for Evolutionary Multiobjective OptimizationIEEE Trans. on Cybernetics, in press, 2015. [pre-print PDF]
  10. B. Zhao, R. Ding; S. Chen, B. Linares-Barranco, and H. Tang. Feedforward Categorization on AER Motion Events using Cortex-like Features in a Spiking Neural NetworkIEEE Trans. on Neural Networks and Learning Systems, vol. 26, no. 9, pp.1963-1978, 2015. [pre-print PDF]
  11. M. Yuan, H. Tang, and H. Li. Real-Time Keypoint Recognition Using Restricted Boltzmann MachineIEEE Trans. on Neural Networks and Learning Systems, vol. 25, no. 11, pp. 2119 - 2126, 2014. [PDF]
  12. H. Tang, K. Ramanathan, and N. Ning. Guest editorial: Special issue on brain inspired models of cognitive memoryNeurocomputing, vol. 138, pp. 1-2, 2014. [PDF]
  13. Q. Yu, H. Tang, K. C. Tan, and H. Yu. A brain-inspired spiking neural network model with temporal encoding and learningNeurocomputing, vol. 138, pp. 3-12, 2014, [PDF]
  14. W. Huang, H. Tang, and B. Tian. Vision Enhanced Neuro-Cognitive Structure for Robotic Spatial CognitionNeurocomputing, vol. 129, pp. 49-58, 2014. [PDF]
  15. Q. Yu, H. Tang, K. C. Tan, and H. Li. Precise-Spike-Driven Synaptic Plasticity: Learning Hetero-Association of Spatiotemporal Spike Patterns. PLoS One 8(11): e78318, 2013. [PDF][Code]
  16. Q. Yu, H. Tang, K. C. Tan, and H. Li. Rapid Feedforward Computation by Temporal Encoding and Learning with Spiking NeuronsIEEE Trans. on Neural Networks and Learning Systems,vol. 24, no. 10, pp. 1539-1552, 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/2014-07-neural-networks-function-human-visual.html#jCp
  17. J. Hu, H. Tang, K.C. Tan, H. Li and L. Shi. A Spike-Timing-Based Integrated Model for Pattern Recognition, vol. 25, no. 2, pp. 450-472, 2013. [PDF]
  18. J. Yu, H. Tang, and H. Li. Dynamics Analysis of A Population Decoding ModelIEEE Trans. on Neural Networks and Learning Systems, vol. 24, no. 3, pp. 498-504, 2013. [PDF]
  19. J. Yu, H. Tang, H. Li, and L. Shi. Dynamical Properties of Continuous Attractor Neural Network with Background TuningNeurocomputing, vol. 99, pp. 439-447, 2013. [PDF]
  20. E.Y. Cheu, J. Yu, C. H. Tan, and H. Tang. Synaptic Conditions for Auto-Associative Memory Storage and Pattern Completion in Jensen et al.'s Model of Hippocampal Area CA3Journal of Computational Neuroscience, vol. 33, no. 3, pp. 435-447, 2012. [PDF] ScienceDaily Report: "Memory-Making Is All About the Connection", 8 Nov 2012.
  21. J. Yu, H. Tang, and H. Li. Continuous Attractors of Discrete-Time Recurrent Neural NetworksNeural Computing and Applications, DOI: 10.1007/s00521-012-0975-5, 2012.
  22. 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 ControlIEEE Computational Intelligence Magazine, vol. 7, no. 1, pp. 64-74, 2012. [PDF] ScienceDaily Report: "Robots Will Quickly Recognize and Respond to Human Gestures, With New Algorithms", 23 May 2012.
  23. H. Tang, H. Li. Book Review: Information Theoretic Learning: Renyi's Entropy and Kernel PerspectivesIEEE Computational Intelligence Magazine, vol. 6, no. 3, pp. 60-62, 2011. PDF
  24. H. Tang, H. Li, and Z. Yi. Online learning and stimulus-driven responses of neurons in visual cortexCognitive Neurodynamics, vol. 5, no. 1, pp. 77-85, 2011. PDF
  25. H. Tang, H. Li, and R. Yan. Memory dynamics in attractor networks with saliency weights.Neural Computation, vol. 22, no. 7, pp. 1899-1926, 2010. PDF
  26. H. Tang, H. Li and Z. Yi. A discrete-time neural network for optimization problems with hybrid constraintsIEEE Trans. on Neural Networks, vol. 21, no. 7, pp. 1184-1189, 2010. PDF
  27. 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 cortexNeuroImage, vol. 47, pp. 157-172, 2009. PDF
  28. H. Tang, L. Weng, Z. Y. Dong and R. Yan. Adaptive and learning control for SI engine model with uncertaintiesIEEE/ASME Trans. on Mechatronics, vol. 14, no. 1, pp. 93-104, 2009. PDF
  29. H. Tang, L. Weng, Z. Y. Dong and R. Yan. Engine control design using globally linearizing control and sliding modeTransactions of the Institute of Measurement and Control, vol. 32, no. 2, pp. 225-247, 2010. PDF
  30. L. Zou, H. Tang, K. C. Tan and W. Zhang. Nontrivial global attractors in 2-D multistable attractor neural networksIEEE Trans. on Neural Networks, vol. 20, no. 11, pp. 1842-1851, 2009.PDF
  31. L. Zou, H. Tang, K. C. Tan and W. Zhang. Analysis of continuous attractors for 2-D linear threshold neural networksIEEE Trans. on Neural Networks, vol. 20, no. 1, pp. 175-180, 2009.PDF
  32. 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 problemNeuroComputing, vol. 71, issue 7-9, pp. 1359-1372, 2008. PDF
  33. H. Qu, Z. Yi and H. Tang. A columnar competitive model for solving multi-traveling salesman problemChaos, Solitons and Fractals, vol. 31, no. 4, pp. 1009-1019, 2007.
  34. H. Qu, Z. Yi and H. Tang. Improving Local Minima of Columnar Competitive Model for TSPs.IEEE Trans. on Circuits and Systems-II, vol. 53, no. 6, pp. 1353-1362, 2006.
  35. H. Tang, K. C. Tan, and E. J. Teoh. Dynamics analysis and analog associative memory of networks with LT neuronsIEEE Trans. on Neural Networks, vol 17, no. 2, pp. 409-418, 2006.PDF
  36. H. Tang, K. C. Tan and W. Zhang. Analysis of cyclic dynamics for networks of linear threshold neuronsNeural Computation, vol. 17, no. 1, pp. 97-114, 2005. PDF
  37. K. C. Tan, H. Tang and S. S. Ge. On parameter settings of Hopfield networks applied to traveling salesman problemsIEEE Trans. on Circuits and Systems - I, vol. 52, no. 5, pp. 994-1002, 2005. PDF
  38. K. C. Tan, H. Tang and W. Zhang. Qualitative analysis for recurrent neural networks with linear threshold transfer functionsIEEE Trans. on Circuits and Systems-I, vol. 52, no. 5, pp. 1003-1012, 2005. PDF
  39. H. Tang, K. C. Tan and Z. Yi. A columnar competitive model for solving combinatorial optimization problemsIEEE Trans. on Neural Networks, vol. 15, no. 6, pp. 1568-1573, 2004.PDF
  40. K. C. Tan, H. J. Tang and Z. Yi. Global exponential stability of discrete-time neural networks for constrained quadratic optimizationNeuroComputing, vol. 56, pp. 399-406, 2004. PDF
  41. K. C. Tan and H. J. Tang. New dynamical optimal learning for linear multilayer FNNIEEE Trans. on Neural Networks, vol. 15, no. 6, pp. 1562-1568, 2004. PDF
  42. 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. 1155-1164, 2004.
  43. Xi Peng, Canyi Lu, Zhang Yi, Huajin Tang. "Connections Between Nuclear-Norm and Frobenius-Norm-Based Representations". IEEE Transactions on Neural Networks and Learning Systems, pp. 1-7, 2016.

CONFERENCE PUBLICATIONS/TALKS

  1. M. Yuan, B. Tian, V. A. Shim, H. Tang and H. Li. An Entorhinal-Hippocampal Model for Simultaneous Cognitive Map Building. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI-15), Austin, TX, USA, 2015. (Acceptance rate: 26.67%) [Oral Presentation (rate 11.75%)] [Pre-print PDF]
  2. X. Peng, Z. Yi and H. Tang. Robust Subspace Clustering via Thresholding Ridge Regression. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI-15), Austin, TX, USA, 2015. (Acceptance rate: 26.67%) [Pre-print PDF] [Codes & Data]
  3. V. A. Shim, B. Tian, M. Yuan, H. Tang, H. Li. Direction-Driven Navigation Using Cognitive Map for Mobile Robots. Proc. of 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Chicago, USA, 2014, pp. 2639-2646. [PDF]
  4. B. H. Tan, H. Tang, R. Yan, and J. Tani. A Flexible and Robust Robotic Arm Design and Skill Learning by Using Recurrent Neural NetworksProc. of 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Chicago, USA, 2014, pp. 522-529. [PDF]
  5. B. Zhao, Q. Yu, H. Yu, S. Chen, H. Tang. A Bio-inspired Feedforward System for Categorization of AER Motion Events. Proc. of IEEE Biomedical Circuits and Systems Conference (BioCAS), Oct 22-24, 2014, Lausanne, Switzerland, pp. 9-12.
  6. B. Tian, V. A. Shim, M. Yuan, C. Srinivasan, H. Tang, H. Li. RGB-D Based Cognitive Map Building and Navigation. Proc. of 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Nov 3-8, Tokyo, Japan, 2013, pp. 1562-1567. [Preprint PDF][Supplementary Video]
  7. Vui Ann Shim, Chris Stephen Naveen Ranjit, Bo Tian, and H. Tang. A Simplified Cerebellum-based Model for Motor Control in Brain Based Devices. 20th International Conference on Neural Information Processing (ICONIP2013), Nov 3-7, Daegu, Korea, 2013. [Preprint PDF]
  8. 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 4-9, Dallas, US, 2013.
  9. J. Hu, H. Tang, and K. C. Tan. A Hierarchical Organized Memory Model Using Spiking Neurons. International Joint Conference on Neural Networks (IJCNN), Aug 4-9, Dallas, US, 2013.
  10. H. Tang, B. Tian, Vui Ann Shim, and K. C. Tan. A Neuro-Cognitive System and Its Application in Robotics. Prof. of 10th IEEE International Conference on Control & Automation (ICCA), IEEE Press, pp. 406 - 411, June 12-14, Hangzhou, China, 2013. [PDF]
  11. 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. 803-807, 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)
  12. J. Hu, H. Tang, and K. C. Tan. Spiking-timing based pattern recognition with real-world visual stimuli. Prof. of IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB), IEEE Press, pp. 23-28, Singapore, April 16-19, 2013.
  13. H. Tang, Q. Yu, and K. C. Tan. Learning Real-World Stimuli by Single-Spike Coding and Tempotron Rule. IEEE World Congress on Computational Intelligence (WCCI), June 10-15, Brisbane, Australia, 2012.
  14. 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 10-15, Brisbane, Australia, 2012.
  15. H. Tang, W. Huang. Brain Inspired Cognitive System for Learning and Memory. 18th International Conference on Neural Information Processing (ICONIP), Nov 14-17, Shanghai, China, 2011.
  16. W. Huang, H. Tang, J. Yu and C. H. Tan. A Neuro-Cognitive Robot for Spatial Navigation. 18th International Conference on Neural Information Processing (ICONIP), Nov 14-17, Shanghai, China, 2011.
  17. 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 14-17, Shanghai, China, 2011.
  18. H. Tang, V. A. Shim, K. C. Tan and J. Y. Chia. Restricted Boltzmann Machine Based Algorithm for Multi-objective Optimization. IEEE World Congress on Computational Intelligence (WCCI), July 18-23, Barcelona , Spain , 2010.
  19. Huajin Tang, Haizhou Li and Zhang Yi. Stimulus-driven responses of neurons in visual cortex exhibit geometrical regularities. 7th International Symposium on Neural Networks (ISNN), June 6-9, Shanghai, China , 2010.
  20. Huajin Tang, C. H. Tan, K. C. Tan. Neural network versus behavior based approach in simulated car racing. IEEE Workshop on Evolving and Self-Developing Intelligent Systems, March 30-April 2, Nashville, TN, USA, 2009.
  21. 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 21-24, Chengdu, China , 2009.
  22. 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 16-21, pp. 3254-3259, 2006.
  23. R. Yan, M. J. Er, and Huajin Tang. An improvement on competitive neural networks applied to image segmentation. Advances  in Neural Networks-ISNN, Chengdu, China, 2006. (Also available in Lecture Notes in Computer Science, vol. 3972, pp. 498-503, 2006).
  24. Huajin Tang, K. C. Tan and T. H. Lee. Dynamical optimal learning for FNN and its applications. FUZZ-IEEE, July 25--29, Budapest, Hungary, 2004.
  25. 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.
  26. Huajin Tang, K. C. Tan and T. H. Lee. Competitive neural networks for solving combinatorial optimization problemsProc. of the Second International Conference on Computational Intelligence, Robotics and Autonomous Systems (CIRAS), Singapore, 2003.
  27. 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. 2470-2475, Honolulu, Hawaii , USA, 2002.
  28. PATENT:Shim Vui Ann, Tian Bo, Yuan Miaolong, Tang Huajin and Li Haizhou. "A Navigation System for Mobile Robots using Direction-Driven With Asymmetrical Multilayered Module", Singapore Filed Patent. No. 10201403296Y, filed 16-Jun-2014.