Selected Ten Publications (updated as of December 2024)
Y. Wu, W. Maass. “A simple model for Behavioral Time Scale Synaptic Plasticity (BTSP) provides content addressable memory with binary synapses and one-shot learning.” Nature Communications, 2025.
Y. Wu, L. Deng, et al. “Adaptive spatiotemporal neural networks through complementary hybridization.” Nature Communications, 2024, 15(1): 7355. (Featured papers)
F. Yu# Y. Wu# (#Equal Contribution), M. Song#, et al. “Brain-inspired multimodal hybrid neural network for robot place recognition”, Science Robotics, May 2023. (Cover paper)
Y. Wu, R. Zhao, J. Zhu, et al. “Brain-inspired global-local learning incorporated with neuromorphic computing.” Nature Communications, 2022, 13(1): 1-14.
R. Zhao#, Y. Zhe#, H. Zheng#, Y. Wu#, et al. “A framework for the general design and computation of hybrid neural networks.” Nature Communications, 2022.
Y. Wu, L. Deng, G. Li, et al. “Direct training for spiking neural networks: faster, larger, better.” In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2019, 33: 1311-1318. (Spotlight)
Y. Wu, L. Deng, G. Li, et al. “Spatio-temporal backpropagation for training high-performance spiking neural networks.” Frontiers in Neuroscience, 2018, 12: 331. (ESI Top 1% Highly Cited Paper)
Z. Zhang#, T. Li#, Y. Wu#, et al. “Truly concomitant and independently expressed short- and long-term plasticity in a Bi₂O₂Se-based three-terminal memristor.” Advanced Materials, 2019, 31(3): 1805769. (Impact Factor: 30.2)
J. Pei, …, Y. Wu, et al. “Towards artificial general intelligence with hybrid Tianjic chip architecture.” Nature, 2019, 572(7767): 106-111. (Cover paper)
L. He, Y. Xu, W. He, Y. Lin, Y. Tian, Y. Wu, et al. “Network model with internal complexity bridges artificial intelligence and neuroscience.” Nature Computational Science, 2024, 4: 8 (Cover runner-up).