| A biologically plausible supervised learning method for spiking neural networks using the symmetric STDP rule Y Hao, X Huang, M Dong, B Xu Neural Networks 121, 387-395, 2020 | 186 | 2020 |
| Recdis-snn: Rectifying membrane potential distribution for directly training spiking neural networks Y Guo, X Tong, Y Chen, L Zhang, X Liu, Z Ma, X Huang Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022 | 102 | 2022 |
| IM-loss: information maximization loss for spiking neural networks Y Guo, Y Chen, L Zhang, X Liu, Y Wang, X Huang, Z Ma Advances in Neural Information Processing Systems 35, 156-166, 2022 | 84 | 2022 |
| Eckpn: Explicit class knowledge propagation network for transductive few-shot learning C Chen, X Yang, C Xu, X Huang, Z Ma Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 74 | 2021 |
| Chaotic motifs in gene regulatory networks Z Zhang, W Ye, Y Qian, Z Zheng, X Huang, G Hu Plos one 7 (7), e39355, 2012 | 70 | 2012 |
| Unsupervised speech recognition through spike-timing-dependent plasticity in a convolutional spiking neural network M Dong, X Huang, B Xu PloS one 13 (11), e0204596, 2018 | 63 | 2018 |
| Direct learning-based deep spiking neural networks: a review Y Guo, X Huang, Z Ma Frontiers in Neuroscience 17, 1209795, 2023 | 55 | 2023 |
| Long-period rhythmic synchronous firing in a scale-free network Y Mi, X Liao, X Huang, L Zhang, W Gu, G Hu, S Wu Proceedings of the National Academy of Sciences 110 (50), E4931-E4936, 2013 | 53 | 2013 |
| Joint a-snn: Joint training of artificial and spiking neural networks via self-distillation and weight factorization Y Guo, W Peng, Y Chen, L Zhang, X Liu, X Huang, Z Ma Pattern Recognition 142, 109639, 2023 | 36 | 2023 |
| Real spike: Learning real-valued spikes for spiking neural networks Y Guo, L Zhang, Y Chen, X Tong, X Liu, YL Wang, X Huang, Z Ma European Conference on Computer Vision, 52-68, 2022 | 36 | 2022 |
| Membrane potential batch normalization for spiking neural networks Y Guo, Y Zhang, Y Chen, W Peng, X Liu, L Zhang, X Huang, Z Ma Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 35 | 2023 |
| Reducing information loss for spiking neural networks Y Guo, Y Chen, L Zhang, YL Wang, X Liu, X Tong, Y Ou, X Huang, Z Ma European Conference on Computer Vision, 36-52, 2022 | 34 | 2022 |
| Rmp-loss: Regularizing membrane potential distribution for spiking neural networks Y Guo, X Liu, Y Chen, L Zhang, W Peng, Y Zhang, X Huang, Z Ma Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 23 | 2023 |
| Ternary spike: Learning ternary spikes for spiking neural networks Y Guo, Y Chen, X Liu, W Peng, Y Zhang, X Huang, Z Ma Proceedings of the AAAI Conference on Artificial Intelligence 38 (11), 12244 …, 2024 | 19 | 2024 |
| A new mesh smoothing method based on a neural network Y Guo, C Wang, Z Ma, X Huang, K Sun, R Zhao Computational Mechanics, 1-14, 2022 | 18 | 2022 |
| Weak higher-order interactions in macroscopic functional networks of the resting brain X Huang, K Xu, C Chu, T Jiang, S Yu Journal of Neuroscience 37 (43), 10481-10497, 2017 | 15 | 2017 |
| Self-sustained oscillations of complex genomic regulatory networks W Ye, X Huang, X Huang, P Li, Q Xia, G Hu Physics Letters A 374 (25), 2521-2526, 2010 | 14 | 2010 |
| Alleviating catastrophic forgetting of incremental object detection via within-class and between-class knowledge distillation M Kang, J Zhang, J Zhang, X Wang, Y Chen, Z Ma, X Huang Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 12 | 2023 |
| Short-term synaptic plasticity expands the operational range of long-term synaptic changes in neural networks G Zeng, X Huang, T Jiang, S Yu Neural Networks 118, 140-147, 2019 | 12 | 2019 |
| Multi-scale expressions of one optimal state regulated by dopamine in the prefrontal cortex G Hu, X Huang, T Jiang, S Yu Frontiers in physiology 10, 113, 2019 | 12 | 2019 |
Shan YuInstitute of Automation, Chinese Academy of SciencesВерификована је имејл адреса на nlpr.ia.ac.cn
