Principal Investigator

Si Wu
Computational Neuroscience, Brain-inspired Computation

address: Address: 5 Yiheyuan Road, Haidian District, Beijing. 100871


 Research Interests:

I am interesting in using mathematical models and computer simulations to elucidate the general principles of neural information processing, and meanwhile, developing brain-inspired computational algorithms. The projects include: the interactive principle of visual information processing, canonic network models of neural information representation, computational roles of short-term synaptic plasticity, multisensory information processing, and models for neuromorphic computing.



Representative Publications


1. Zhang, W. H.,A.H. Chen, M.J. Rasch*, and S. Wu* (2016). Decentralized Multisensory Information Integration in Neural Systems. Journal of Neuroscience, January 13, 2016. 36(2):532–547.

2. Xiaolan Wang, C.C. Alan Fung, Shaobo Guan, Si Wu*, Michael E. Goldberg, Mingsha Zhang*. Perisaccadic Receptive Field Expansion in the Lateral Intraparietal Area. Neuron, Volume 90, Issue 2, 20 April 2016, Pages 400–409. doi:10.1016/j.neuron.2016.02.035.

3. W. Zhang, H. Wang, KYM Wong, S. Wu* (2016). “Concurrent” and “Opposite” Neurons: Sisters for Multisensory Integration and Segregation. Advances in Neural Information Processing Systems (NIPS*2016).

4. Y. Mi, C. C. Alan Fung, K. Y. Michael Wong, S.Wu*(2014).Spike Frequency Adaptation Implements Anticipative Tracking in Continuous Attractor Neural Networks. Advances in Neural Information Processing Systems (NIPS*2014).

5. Y. Mi, L. Li, D. Wang, S.Wu*(2014). A Synaptic Story of Persistent Activity with Graded Lifetime in a Neural System. Advances in Neural Information Processing Systems (NIPS*2014).

6. 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.

7. 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.

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

9. 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.

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



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