吴思教授的研究领域为计算神经科学和类脑计算，通过和认知科学家、神经科学家、信息科学家等合作，用数理方法和计算机仿真来构建神经系统加工信息的计算模型，阐明大脑处理信息的一般性原理，并在此基础上发展类脑的人工智能算法。已发表论文上百篇，包括神经科学的顶级杂志Neuron，Nature Neuroscience、PNAS、eLfie、J. Neurosci.等，人工智能的顶级国际会议NeurIPS等。目前在计算神经科学领域开展的课题包括：神经信息表达与储存的正则化模型-连续吸引子网络的计算性质、突触短时程可塑性的计算功能、神经反馈的计算功能、多模态信息整合的计算机制等；在类脑计算领域开展的课题包括：视觉信息从整体到局部加工的计算模型、时空信息加工的计算模型、运动目标预测跟踪的计算模型等。目前也正研发计算神经科学与类脑计算的科研与教学的通用软件平台BrainPy。

代表性论文（*通讯作者）：

Chaoming Wang#, Tianqiu Zhang#, Sichao He, Hongyaoxing Gu, Shangyang Li, Si Wu*(2024). A differentiable brain simulator bridging brain simulation and brain-inspired computing, ICLR, 2024.

Chu, T., Ji, Z., Zuo, J., Mi, Y., Zhang, W., Huang, T., ... & Wu Si * (2024). Firing rate adaptation affords place cell theta sweeps, phase precession and procession. https://doi.org/10.7554/eLife.87055.1, eLife, 2024, 87055.4.

Chaoming Wang，Tianqiu Zhang，Xiaoyu Chen，Sichao He，Shangyang Li，Si Wu* (2023). BrainPy, a flexible, integrative, efficient, and extensible framework for general-purpose brain dynamics programming, https://doi.org/10.7554/eLife.86365, eLife, 2023,86365.

Xingsi Dong, Wu Si* (2023). Neural Sampling in Hierarchical Exponential-family Energy-based Models, NeurIPS, 2023.

Xiaohan Lin, Liyuan Li, Boxin Shi, Tiejun Huang, Yuanyuan Mi, Wu Si* (2023). Slow and Weak Attractor Computation Embedded in Fast and Strong E-I Balanced Neural Dynamics, NeurIPS, 2023.

Zuo, J., Liu, X., Wu, Y., Wu, S. and Zhang, W.H. *(2023). A Recurrent Neural Circuit Mechanism of Temporal-scaling Equivariant Representation, NeurIPS,2023.

Xiaolong Zou#, Zhikun Chu#, Qinghai Guo, Jie Cheng, Bo Hong, Si Wu, Yuanyuan Mi* (2022). Learning and Processing the Ordinal Information of Temporal Sequences in Recurrent Neural Circuits, NeurIPS, 2023.

Zou, Xiaolong and Ji, Zilong and Zhang, Tianqiu and Huang, Tiejun and Wu Si* (2023). Visual information processing through the interplay between fine and coarse signal pathways, https://doi.org/10.1016/j.neunet.2023.07.048, Neural Networks, 2023,166(692-703).

Zhang, W.H., Wu Si, Josić, K. and Doiron, B., (2023). Sampling-based Bayesian inference in recurrent circuits of stochastic spiking neurons, Nature Communications, 2023, 14(1), p.7074.

Liu, X., Zou, X., Ji, Z., Tian, G. Mi, Y., Huang, T., Wong, M.*, & Wu Si* (2022). Neural feedback facilitates rough-to-fine information retrieval, Neural Networks, 2022,151(349-364).

Ang A.Li, Fengchao Wang, Wu Si*, Xiaohui Zhang* (2022). Emergence of probabilistic representation in the neural network of primary visual cortex, iScience, 2022,25(3).

Tianhao Chu, Zilong Ji, Junfeng Zuo, Wenhao Zhang, Tiejun Huang, Yuanyuan Mi, Wu Si* (2022). Oscillatory Tracking of Continuous Attractor Neural Networks Account for Phase Precession and Procession of Hippocampal Place Cells, NeurIPS, 2022.

Xingsi Dong, Zilong Ji, Tianhao Chu, Tiejun Huang, Wenhao Zhang*, Wu Si* (2022). Adaptation Accelerating Sampling-based Bayesian Inference in Attractor Neural Networks, NeurIPS, 2022.

XiaoLong Zou,Tie-Jun Huang, Wu Si* (2022). Towards a New Paradigm for Brain-inspired Computer Vision. DOI:10.1007/s11633-022-1370-z, Machine Intelligence Research, 2022,19(5), P412-424.

Wenhao Zhang*, Yinnian Wu, Si Wu (2022). Translation-equivariant Representation in Recurrent Networks with a Continuous Manifold of Attractors, NeurIPS, 2022.

Xiaohan Lin#, Xiaolong Zou#, Zilong Ji, Tiejun Huang, Si Wu*, Yuanyuan Mi*, A brain-inspired computational model for spatio-temporal information processing, Neural Networks, 2021, 143:74-87.

W. Zhang, H. Wang, A. Chen, Y. Gu, T. S. Lee, KYM Wong*, S. Wu* (2019). Complementary congruent and opposite neurons achieve concurrent multisensory integration and segregation. eLife 8: e43753.

X. Liu, X. Zou, Z. Ji, G. Tian, Y. Mi, T. Huang, KYM Wong, S. Wu* (2019). Push-pull feedback implements hierarchical information retrieval efficiently. NeurIPS, 2019.

W.H. Zhang, S. Wu, B. Doiron, T.S. Lee. A Normative Theory for Causal Inference and Bayes Factor Computation in Neural Circuits. NeurIPS, 2019.

Jing Pei, Lei Deng, Sen Song, Mingguo Zhao, Youhui Zhang, Shuang, Guanrui Wang, Zhe Zou, Zhenzhi Wu , Wei He, Feng Chen, Ning Deng, Si Wu, Yu Wang, Yujie Wu, Zheyu Yang, Cheng Ma, Guoqi Li, Wentao Han, Huanglong Li, Huaqiang Wu, Rong Zhao, Yuan Xie, Luping Shi (2019) . Towards artificial general intelligence with hybrid Tianjic chip architecture. Nature v. 572; https://doi.org/10.1038/s41586-019-1424-8. (连续吸引子模型应用在天机芯片上).

Xiaolan Wang, C.C. Alan Fung, Shaobo Guan, Si Wu*, Michael E. Goldberg, Mingsha Zhang* (2016). Perisaccadic Receptive Field Expansion in the Lateral Intraparietal Area. Neuron, 90(2): 400–409.

W. Zhang, A. Chen, M. Rasch* and S. Wu* (2016). Decentralized multi-sensory information integration in neural systems. The Journal of Neuroscience, 36(2):532-547.

W. Zhang, H. Wang, KYM Wong, S. Wu* (2016). “Concurrent” and “Opposite” Neurons: Sisters for Multisensory Integration and Segregation. NeurIPS, 2016.

Wu, S*, Wong, KYM., Fung, CCA., Mi, Y., and Zhang, W. (2016). Continuous attractor neural networks: candidate of a canonical model for neural information representation. F1000 Invited Review, 66(16), 209-226.

Y. Yan, M. Rasch, M. Chen, X. Xiang, M. Huang, S. Wu and W. Li (2014). Perceptual training continuously refines neuronal population codes in primary cortex. Nature Neuroscience17: 1380–1387. doi:10.1038/nn.3805.

Y. Mi, C. C. Alan Fung, K. Y. Michael Wong, S.Wu*(2014).Spike Frequency Adaptation Implements Anticipative Tracking in Continuous Attractor Neural Networks. NeurIPS, 2014.

Y. Mi , X. Liao , X. Huang , L. Zhang , W. Gu, G. Hu* and S. Wu* (2013). Long-Period Rhythmic Synchronous Firing in a Scale-Free Network. Proc. Natl. Acad. Sci. USA 110:E4931-4936.

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. doi:10.1152/jn.00221.2013.

Tsodyks, M. and Wu, S* (2013). Short-term synaptic plasticity. Scholarpedia, 8(10):3153.

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, 2012.

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.

C. C. Fung, K. Y. Michael Wong, H. Wang and S.Wu* (2010). Attractor Dynamics with Synaptic Depression. NeurIPS 2010.

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.

C. C.Fung, K.Y.Michael Wong and S. Wu* (2008). Tracking Changing Stimuli in Continuous Attractor Neural Networks. NeurIPS 2008.

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

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.

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 and S. Amari. (2002). Neural Implementation of Bayesian Inference in Population Codes. NeurIPS 2002.

S. Wu, H. Nakahaara, N. Murata and S. Amari. (2000). Population Decoding Based on an Unfaithful Model. NeurIPS 2000.

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

 

计算神经科学与类脑计算的开源软件平台：

BrainPy：https://github.com/PKU-NIP-Lab/BrainPy