[39] Shikuang Deng, Yuhang Li, Shanghai Zhang, and Shi Gu. Temporal efficient training of spiking neural network via gradient re-weighting. In International Conference on Learning Representations (ICLR), 2022.
[40] Wei Fang, Zhaofei Yu, Yanqi Chen, Tiejun Huang, Timothée Masquelier, and Yonghong Tian. Deep residual learning in spiking neural networks. Advances in Neural Information Processing Systems (NeurIPS), 34, 2021.
[41] Zhaokun Zhou, Yuesheng Zhu, Chao He, Yaowei Wang, Shuicheng YAN, Yonghong Tian, and Li Yuan. Spikformer: When spiking neural network meets transformer. In International Conference on Learning Representations (ICLR), 2023.
[42] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in Neural Information Processing Systems (NeurIPS), 30, 2017.
[43] Rajat Raina, Anand Madhavan, and Andrew Y Ng. Large-scale deep unsupervised learning using graphics processors. In International Conference on Machine Learning (ICML), pages 873-880, 2009.
[44] Nal Kalchbrenner, Lasse Espeholt, Karen Simonyan, Aaron van den Oord, Alex Graves, and Koray Kavukcuoglu. Neural machine translation in linear time. arXiv preprint arXiv:1610.10099, 2016.
[45] Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N Dauphin. Convolutional sequence to sequence learning. In International Conference on Machine Learning (ICML), pages 1243-1252. PMLR, 2017.
[46] Eric Martin and Chris Cundy. Parallelizing linear recurrent neural nets over sequence length. In International Conference on Learning Representations (ICLR), 2018.
[47] Mark Harris, Shubhabrata Sengupta, and John D Owens. Parallel prefix sum (scan) with CUDA. GPU gems, 3(39):851-876, 2007.
[48] Eimantas Ledinauskas, Julius Ruseckas, Alfonsas Juršenas, and Giedrius Burăcas. Training Deep Spiking Neural Networks. arXiv preprint arXiv:2006.04436, 2020.
[49] Seongsik Park, Seijoon Kim, Byunggook Na, and Sungroh Yoon. T2fsnn: deep spiking neural networks with time-to-first-spike coding. In 2020 57th ACM/IEEE Design Automation Conference, pages 1-6. IEEE, 2020.
[50] Dongwoo Lew, Kyungchul Lee, and Jongsun Park. A time-to-first-spike coding and conversion aware training for energy-efficient deep spiking neural network processor design. In Proceedings of the 59th ACM/IEEE Design Automation Conference, pages 265–270, 2022.
[51] Lina Bonilla, Jacques Gautrais, Simon Thorpe, and Timothée Masquelier. Analyzing time-to-first-spike coding schemes. Frontiers in Neuroscience, 16, 2022.
[52] Wei Fang, Yanqi Chen, Jianhao Ding, Zhaofei Yu, Timothee Masquelier, Ding Chen, Liwei Huang, Huihui Zhou, Guoqi Li, and Yonghong Tian. Spikingjelly: An open-source machine learning infrastructure platform for spike-based intelligence. Science Advances, 9(40):eadi1480, 2023.
[53] Friedemann Zenke and Tim P Vogels. The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks. *BioRxiv*, 2020.
[54] Hongmin Li, Hanchao Liu, Xiangyang Ji, Guoqi Li, and Luping Shi. Cifar10-dvs: An event-stream dataset for object classification. Frontiers in Neuroscience, 11, 2017.
[55] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In Proceedings of the IEEE international conference on computer vision, pages 1026-1034, 2015.