吳思檢視原始碼討論檢視歷史

吳思
原圖鏈接 圖片來源百科 北京師範大學教授
國籍	中國
職業	教育科研工作者

吳思，男，漢族，北京師範大學認知神經科學與學習國家重點實驗室，教授，博士生導師。中國自動化學會生物控制和醫學工程分會委員會主任。

人物經歷

工作經歷

2011.9--- 北京師範大學認知神經科學與學習國家重點實驗室，教授。

2008.7---2011.8: 中國科學院神經科學研究所，神經信息處理課題組組長，研究員(百人計劃)。

2005.1---2008.6: 英國Sussex大學信息科學系，生物信息與機器學習研究室主任。

2006.6---2008.6: 英國Sussex大學信息科學系，計算神經生物學高級講師(副教授)。

2003.3---2006.5: 英國Sussex大學信息科學系，講師。

2000.9---2003.2: 英國Sheffield大學計算科學系，講師(助理教授)。

1998.6---2000.8: 日本理化學所腦科學研究所，數學神經科學實驗室博士後。

1997.6---1998.5: 比利時Limburg University Center物理系，博士後。

1995.9---1997.5: 香港科技大學物理系，博士後。

學習經歷

1992.9---1995.7: 北京師範大學統計物理學，博士。

1990.9---1992.7: 北京師範大學廣義相對論，碩士。

1987.9---1990.7: 北京師範大學物理學，學士。

學術任職

Neural Networks (action editor)。

Frontiers in Computational Neuroscience (associative editor)。

Faculty of 1000 (faculty member of Theoretical & Computational Neuroscience)。

中國自動化學會生物控制和醫學工程分會委員會主任。

研究方向

計算神經科學 (1998-至今)。

機器學習 (1998-至今)。

智能通訊網絡控制 (1995-1997)。

統計物理和廣義相對論 (1990-1995)。

主要貢獻

論文與著作:

計算神經科學 (Computational Neuroscience)

2013年

Yuanyuan Mi , Xuhong Liao , Xuhui Huang , Lisheng Zhang , Weifeng Gu , Gang Hu , Si Wu* (2013).Long-Period Rhythmic Synchronous Firing in a Scale-Free Network，Proc. Natl. Acad. Sci. USA(in press).

Tsodyks, M. and Wu, S.* (2013). Short-term synaptic plasticity. Scholarpedia, 8(10):3153. doi:10.4249/scholarpedia.3153: www.scholarpedia.org/article/Short-term_synaptic_plasticity.

Zhang D, Li Y, Wu S and Rasch MJ (2013). Design principles of the sparse coding network and the role of "sister cells" in the olfactory system of Drosophila. Front. Comput. Neurosci. 7:141.

W. Zhang and S. Wu*. Reciprocally Coupled Local Estimators Implement Bayesian Information Integration Distributively. Advances in Neural Information Processing Systems (NIPS) 2013, (In press).

L. Xiao, M. Zhang, D. Xing, P-J. Liang* and S. Wu* (2013).Shift of encoding strategy in retinal luminance adaptation: from firing rate to neural correlation. Journal of Neurophysiology 110: 1793-1803.

W. Zhang and S. Wu *(2013). Trade-off between Decoding Accuracy and Tracking Speed in Neural Networks. IJCAI-WIS2013, Beijing.2013 (in press).

Danke Zhang, Xichun Zhang, Malte Rasch, Si Wu * (2013). Divisive Normalization by Shunting Inhibition in Neural Networks. IJCAI-WIS2013, Beijing.2013(in press).

Danke Zhang, Yuanqing Li, Si Wu * (2013). Concentration-Invariant Representation in the Olfactory System by Presynaptic Inhibition. Computational and Mathematical Methods in Medicine. 2013.

Rasch*, M. J., M. Chen, S. Wu, H. D. Lu, and A. W. Roe* (2013). Quantitative inference of population response properties across eccentricity from motion-induced maps in macaque V1. Journal of Neurophysiology 109(5), 1233–1249.

Fung CCA, Wang H, Lam K, Wong KYM* and Wu S* (2013). Resolution enhancement in neural networks with dynamical synapses. Front. Comput. Neurosci. 7:73. doi: 10.3389/fncom.2013.00073.

Zhang D, Li Y, Rasch MJ* and Wu S* (2013) Nonlinear multiplicative dendritic integration in neuron and network models. Front. Comput. Neurosci. 7:56. doi: 10.3389/fncom.2013.00056.

Xiao Lei, Zhang Danke, Li Yuanqing, Liang Peiji* and Wu Si* (2013) Adaptive neural information processing with dynamical electrical synapses. Frontiers in Computational Neuroscience 7(36) doi: 10.3389\fncom.2013.00036.

2012年

C. C. Fung, K. Y. Michael Wong* and S. Wu* (2012). Delay Compensation with Dynamical Synapses. Advances in Neural Information Processing Systems 16, NIPS*2012.

W. Zhang and S. Wu* (2012). Neural Information Processing with Feedback Modulations. Neural Computation 24(7): 1695-1721.

C. C. Fung, K. Y. Michael Wong, H. Wang and S. Wu* (2012). Dynamical Synapses Enhance Neural Information Processing: Gracefulness, Accuracy and Mobility. Neural Computation 24(5):1147-1185.

2011年

L. Huang, Y. Cui, D. Zhang and S. Wu* (2011). Impact of noise structure and network topology on tracking speed of neural networks. Neural Networks, v.24, 1110-1119.

L. Ma, M. J. Rasch and S. Wu (2011). Learning Variance Statistics of Natural Images. ISNN 2011, Part II, LNCS 6676, pp. 429–436.

K. Cai, J. Shen and S. Wu (2011). Decision-making in Drosophia with two conflicting cues. ISNN 2011.

M. J. Rasch and S. Wu (2011). A Minimal Feedback Model of Visual Cortices V1 and V2: Input-Output Relations. ISNN 2011.

2010年

C. C. Fung, K. Y. Michael Wong, H. Wang and S.Wu (2010). Attractor Dynamics with Synaptic Depression. Advances in Neural Information Processing Systems 24, NIPS*2010.

S. Wu and P. Liang (2010). Computational Neuroscience in China. Science China: Life Sciences, 53, 385-397.

C. C.Fung, K.Y.Michael Wong and S. Wu (2010). A Moving Bump in a Continuous Manifold: A Comprehensive Study of the Tracking Dynamics of Continuous Attractor Neural Networks. Neural Computation, v.22, p.752-792.

L. Huang and S. Wu (2010). Stimulus-Dependent Noise Facilitates Tracking Performances of Neuronal Networks. ISSN*2010.

2009年

S. Wu and L. Ma (2009). Compensating Neural Transmission Delays by Dynamical Routing. ICCN'09.

S. Wu and S. Amari (2009). On the Condition for Fast Neural Computation. 48th CDC /28th CCC.

C. C.Fung,K.Y.Michale Wong and S. Wu (2009). Dynamics of Two Dimensional Continuous Attractor Neural Networks. Jounral of Physics: Conference Series 197, 012017(10 pages).

R. Nijhawan and S. Wu (2009). Compensating Time Delays with Neural Predictions: Are Predictions Sensory or Motor? Phil. Trans. R. Soc. A , v.367, p.1063-1078.

2008年及2008年之前

C. C.Fung,K.Y.Michale Wong and S. Wu (2008). Dynamics of Neural Networks with Continuous Attractors. Europhysics Letter, v.84, 18002.

C. C.Fung,K.Y.Michale Wong and S. Wu (2008). Tracking Changing Stimuli in Continuous Attractor Neural Networks. Advances in Neural Information Processing Systems 22, NIPS*2008 ,p.481-488.

S. Wu (2007). Behaviour Signatures of Continuous Attractors (ICCN'07).

S. Wu and T. Trappenberg (2007). Learning in sparse attractor networks with inhabitation (ICCN'07).

S. Wu, K. Hamaguchi and S. Amari (2008). Dynamics and Computation of Continuous Attractors. Neural Computation, v.20, 994..

S. Wu, K. Hamaguchi and S. Amari (2007). The Tracking Speed of Continuous Attractors. ISNN*07 ,v.4491,p.926-934.

S. Li and S. Wu(2007) Robustness of Neural Coding and Its Implications on Natural Image Processing. Cognitive Neurodynamics , v.1(3): 261–272.

S. Wu, F. Feng and S. Amari (2006). The Ideal Noisy Environment for Fast Neural Computation. ISSN(1) 2006, 1-6 .

S. Wu and S. Amari (2005). Computing with Continuous Attractors: Stability and On-Line Aspects. Neural Computation, v.17, 2215-2239.

S. Wu, S. Amari and H. Nakahara. (2004). Information Processing in a Neuron Ensemble with the Multiplicative Correlation Structure. Neural Networks, v.17, p.205-214.

S. Li and S. Wu (2004) . On the variability of cortical neural responses: a statistical interpretation. Neurocomputing, v.65, p.409-414.

S. Wu, D. Chen, M. Niranjan and S. Amari. (2003). Sequential Bayesian Decoding with a Number of Neurons. Neural Computation, v.15, p.993-1013.

S. Amari, H. Nakahara, S. Wu and Y. Sakai (2003). Synfiring and Higher-Order Interactions in Neuron Pool. Neural Computation, v.15, p. 127-142.

S. Wu and D. Chen (2002). Computation with a Number of Neurons. In Proceeding of the Workshop in Sheffield: Uncertainty in Geometry Computation.

S. Wu and S. Amari. (2002). Neural Implementation of Bayesian Inference in Population Codes. Advances in Neural Information Processing Systems 14 (NIPS*2001).

S. Wu, S. Amari and H. Nakahara. (2002). Population Coding and Decoding in a Neural Field: A Computational Study. Neural Computation, v14, no.5, p.999-1026.

S. Wu, S. Amari and H. Nakahara. (2001). Asymptotic Behaviors of Population Codes. Presented in the 10th Annual Computational Neuroscience Meeting (CNS*01), Sanfrasco, USA. Neural computing, v.57,p.373-387.

S. Wu, H. Nakahara and S. Amari. (2001). Population Coding with Correlation and an Unfaithful Model. Neural Computation, v.13, p.775-798.

H. Nakahara, S. Wu and S. Amari. (2001). Attention Modulation of Neural Tuning Through Peak and Base Rate. Neural Computation, v.13, no. 9.

S. Wu, Danmei Chen and S. Amari. (2000). Unfaithful Population Coding, IJCNN2000, Como, Italy.

S. Wu, H. Nakahaara, N. Murata and S. Amari. (2000). Population Decoding Based on an Unfaithful Model. Advances in Neural Information Processing Systems 12 (NIPS'99), pp.192-198, 2000.

S. Wu and H. Nakahara. (1999). Optimize the Distribution of Preferred Stimulus in a Population Code. Proc. International Conference on Neural Information Processing (ICONIP'99), pp. 325-329.

機器學習 (Machine Learning):

W.He and S. Wu (2012) A kernel-based Perceptron with dynamical memory. Neural Networks, v.25, 106-113.

A. Gretton, K. Borgwardt, M.J. Rasch, B. Scholkopf, A. Smola (2012). A Kernel Two-Sample Test. Journal of Machine Learning Research 13 (2012) 723-773.

D.Chen, S. Li, Z. Kourtzi and S. Wu (2010). Behavior-constrained support vector machines for fMRI data analysis. IEEE Trans. Neural Networks. v. 21, 1680-1685.

P.Williams, S. Li, J. Feng and S. Wu (2007). A Geometric Method to Improve Performance of the Support Vector Machine. IEEE Trans. On Neural Networks, v.3, 942-947.

P.Williams, S. Li, J. Feng and S. Wu (2006). Scaling the Kernel Function to Improve Performance of the Support Vector Machine. Lecture Notes on Computer Science, ISSN (1), 2006, 831-836/.

P. Williams, S. Wu and J. Feng (2005). Two Scaling Methods to Improve Performance of the Support Vector Machine. Invited book chapter in Support Vector Machine: Theory and Application.

S. Wu and S. Amari. (2002). Conformal Transformation of Kernel Functions: A Data-Dependent Way to Improve the Performance of Support Vector Machine Classifiers. Neural Processing Letter, v15, no.1.

S. Amari and S. Wu (1999). Improving Support Vector Machine Classifiers by Modifying Kernel Functions Neural Networks, v.12, p.783-789, 1999.

S. Amari and S. Wu (1999). An Information-Geometrical Method for Improving the Performance of Support Vector Machine Classifiers ICANN'99, p.85-91.

S. Vijayakumar and S. Wu (1999). Sequential Support Vector Classifiers and Regression, Proc. International Conference on Soft Computing (SOCO'99), pp.610-619.

S. Vijayakumar and S. Wu, A gradient based technique for generating sparse representation in function approximation. Proc. International Conference on Neural Information Processing (ICONIP'99), pp.314-319 (1999).

S. Wu and C. Van den Broeck, A Supervised Radial Basis Function Neural Network, European Symposium on Artificial Neural Networks (ESANN*98), pp.7-12, 1998.

智能通訊網絡管理 (Intelligent Network Management):

S. Wu and K. Y. Michael Wong. (2002). Neural Networks: Techniques and Applications in Telecommunications Systems. (Invited Book Chapter: Intelligence Systems Techniques and Applications, CRC Press.).

S. Wu and K. Y. Michael Wong and B. Li. (2002). A Dynamic Call Admission Policy for Precision QoS Guarantee Using Stochastic Control for Mobile Wireless Networks. IEEE/ACM Transactions on Networking, v.10, p.257-271.

B. Li, Y. Li, K.Y.M.Wong and S. Wu (2001). An Efficient and Adaptive Bandwidth Allocation Scheme for Mobile Wireless Networks Based on On-line Local Estimation Technique. ACM/Baltzer Journal of Wireless Networks, v.7, p.107-116.

B. Li, Y. Li, K. Y. M. Wong and S. Wu (1999). An Efficient and Adaptive Bandwidth Allocation Scheme for Mobile Wireless Networks Based on On-line Local Parameters Estimation. The 10th International IEEE Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC'99).

S. Wu and K. Y. Michael Wong (1998). Dynamic Overload Control for Distributed Call Processors Using the Neural Networks Method. IEEE Transactions on Neural Networks, v9, No.6.

S. Wu, K. Y. Michael Wong and B. Li (1998). "A Stable, Distributed Dynamic Call Admission Control for Mobile Wireless Networks with QoS Guarantee: The Single Traffic Case", IEEE Globecom98.

S. Wu, K.Y.M.Wong and B.Li (1998). A New, Distributed Dynamic Call Admission Policy for Mobile Wireless Networks with QoS Guarantee, Ninth International IEEE Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC'98) CD-ROM: 398a011 (5 pages).

S. Wu and K. Y. Michael Wong (1997). Neural Networks for Distributed Overload Control in Telecommunications Networks. Fifth International Conference on Artificial Neural Networks, pp.312-317.

S. Wu and K. Y. Michael Wong (1997). Overload Control in Telecommunications Networks Using Neural Networks Method, Proceedings of the International Workshop on Applications of Neural Networks in Telecommunications 3 (IWANNT*97), pp.149-156.

統計物理 (Statistical Physics):

S. Wu and Z. R. Yang (1994). Self-avoiding Random Walks on a Family of Diamond-type Hierarchical Lattice, Phys. Rev. E, v49, 4700(1994).

S. Wu and Z. R. Yang (1995). On the Role of Spectral Dimension in Determining Phase Transition, J. Phys. A, v28, 6161(1995).

S. Wu and Z. R. Yang (1995). Bond Percolation on Branching Koch Curves, J. Phys. A, v28, 2729(1995).

Danmei Chen, S. Wu, Ai-ke Guo and Z.R. Yang (1995). Self-organized Criticality in a Cellular-Autimaton Model of Pulse-Coupled Integrate-and-Fire Neurons, J.Phys.A, v28, 5177(1995).

S. Wu and Z. R. Yang, Kawasaki Dynamics on Square Lattice (1995). Communication in Theoretic Physics, v35, 1423(1995).

廣義相對論 (General Relativity):

S. Wu and Z. Zhao. The Quantum Thermal Effect of a Non-uniformly Rectilinearly Accelerating Kerr Black Hole (in Chinese), ACTA ASTRONOMICA SINICA, v34, 17(1993).

S. Wu and Z. Zhao, The Singular Momentum of Black Hole (in Chinese), CHINESE SCIENCE BULLETIN, v38, 890(1993).