| Training high-performance low-latency spiking neural networks by differentiation on spike representation Q Meng, M Xiao, S Yan, Y Wang, Z Lin, ZQ Luo Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022 | 132 | 2022 |
| Training feedback spiking neural networks by implicit differentiation on the equilibrium state M Xiao, Q Meng, Z Zhang, Y Wang, Z Lin Advances in neural information processing systems 34, 14516-14528, 2021 | 64 | 2021 |
| Online training through time for spiking neural networks M Xiao, Q Meng, Z Zhang, D He, Z Lin Advances in neural information processing systems 35, 20717-20730, 2022 | 52 | 2022 |
| Training much deeper spiking neural networks with a small number of time-steps Q Meng, S Yan, M Xiao, Y Wang, Z Lin, ZQ Luo Neural Networks 153, 254-268, 2022 | 28 | 2022 |
| Towards memory-and time-efficient backpropagation for training spiking neural networks Q Meng, M Xiao, S Yan, Y Wang, Z Lin, ZQ Luo Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 22 | 2023 |
| SPIDE: A purely spike-based method for training feedback spiking neural networks M Xiao, Q Meng, Z Zhang, Y Wang, Z Lin Neural Networks 161, 9-24, 2023 | 12 | 2023 |
| Sampling complex topology structures for spiking neural networks S Yan, Q Meng, M Xiao, Y Wang, Z Lin Neural Networks 172, 106121, 2024 | 3 | 2024 |
| Hebbian learning based orthogonal projection for continual learning of spiking neural networks M Xiao, Q Meng, Z Zhang, D He, Z Lin arXiv preprint arXiv:2402.11984, 2024 | 1 | 2024 |
| Online Pseudo-Zeroth-Order Training of Neuromorphic Spiking Neural Networks M Xiao, Q Meng, Z Zhang, D He, Z Lin arXiv preprint arXiv:2407.12516, 2024 | | 2024 |
| Forward Gradient Training of Spiking Neural Networks M Xiao, Q Meng, Z Zhang, D He, Z Lin | | |
| Supplementary Materials for: Towards Memory-and Time-Efficient Backpropagation for Training Spiking Neural Networks Q Meng, M Xiao, S Yan, Y Wang, Z Lin, ZQ Luo | | |
| Supplementary Materials for: Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State M Xiao, Q Meng, Z Zhang, Y Wang, Z Lin | | |
| Supplementary Materials for: Online Training Through Time for Spiking Neural Networks M Xiao, Q Meng, Z Zhang, D He, Z Lin | | |
