Wei Fang's Homepage


Research Assistant Professor, School of Electronic and Computer Engineering, Peking University

Wei Fang’s Homepage

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I am currently the Research Assistant Professor in the School of Electronic and Computer Engineering, Peking University.

I received my Ph.D. degree in School of Computer Science, Peking University, supervised by Prof. Yonghong Tian. I have been cooperating with Prof. Zhaofei Yu of Peking University, Researcher Timothée Masquelier of CNRS, and Prof. Guoqi Li of Institute of Automation, Chinese Academy of Sciences.

My recent research interests are learning and network structure of Spiking Neural Network.

Please contact me (fwei at pku.edu.cn) if you are interested in my research.

Education and Working Experience


First/Corresponding-author Papers

  1. Wei Fang, Zhaofei Yu, Yanqi Chen, Timothée Masquelier, Tiejun Huang, Yonghong Tian, Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks, ICCV 2021
  2. Wei Fang, Zhaofei Yu, Yanqi Chen, Tiejun Huang, Timothée Masquelier, Yonghong Tian, Deep Residual Learning in Spiking Neural Networks, NeurIPS 2021
  3. Wei Fang, Yanqi Chen, Jianhao Ding, Zhaofei Yu, Timothée Masquelier, Ding Chen, Liwei Huang, Huihui Zhou, Guoqi Li, Yonghong Tian, SpikingJelly: An Open-source Machine Learning Infrastructure Platform for Spike-based Intelligence, Science Advances
  4. Wei Fang, Zhaofei Yu, Zhaokun Zhou, Ding Chen, Yanqi Chen, Zhengyu Ma, Timothée Masquelier, Yonghong Tian, Parallel Spiking Neurons with High Efficiency and Ability to Learn Long-term Dependencies, NeurIPS 2023

Other Papers

  1. Yanqi Chen, Zhaofei Yu, Wei Fang, Tiejun Huang, Yonghong Tian, Pruning of Deep Spiking Neural Networks through Gradient Rewiring, IJCAI 2021
  2. Tong Bu, Wei Fang, Jianhao Ding, PengLin Dai, Zhaofei Yu, Tiejun Huang, Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks, ICLR 2022
  3. Yanqi Chen, Zhaofei Yu, Wei Fang, Zhengyu Ma, Tiejun Huang, Yonghong Tian, State Transition of Dendritic Spines Improves Learning of Sparse Spiking Neural Networks, ICML 2022
  4. Yaoyu Zhu, Zhaofei Yu, Wei Fang, Xiaodong Xie, Tiejun Huang, Timothée Masquelier, Training Spiking Neural Networks with Event-driven Backpropagation, NeurIPS 2022
  5. Yanqi Chen, Zhengyu Ma, Wei Fang, Xiawu Zheng, Zhaofei Yu, Yonghong Tian, A Unified Framework for Soft Threshold Pruning, ICLR 2023
  6. Yaoyu Zhu, Wei Fang, Xiaodong Xie, Tiejun Huang, Zhaofei Yu, Exploring Loss Functions for Time-based Training Strategy in Spiking Neural Networks, NeurIPS 2023
  7. Liuzhenghao Lv, Wei Fang, Li Yuan, Yonghong Tian, Optimal ANN-SNN Conversion with Group Neurons, ICASSP 2024
  8. Man Yao, Ole Richter, Guangshe Zhao, Ning Qiao, Yannan Xing, Dingheng Wang, Tianxiang Hu, Wei Fang, Tugba Demirci, Michele De Marchi, Lei Deng, Tianyi Yan, Carsten Nielsen, Sadique Sheik, Chenxi Wu, Yonghong Tian, Bo Xu, Guoqi Li, Spike-based Dynamic Computing with Asynchronous Sensing-Computing Neuromorphic Chip, Nature Communications
  9. Haonan Qiu, Munan Ning, Zeyin Song, Wei Fang, Yanqi Chen, Tao Sun, Zhengyu Ma, Li Yuan, Yonghong Tian, Self-architectural Knowledge Distillation for Spiking Neural Networks, Neural Networks