[1] Arnon Amir, Brian Taba, David Berg, Timothy Melano, Jeffrey McKinstry, Carmelo Di Nolfo, Tapan Nayak, Alexander Andreopoulos, Guillaume Garreau, Marcela Mendoza, et al. A low power, fully event-based gesture recognition system. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7243-7252, 2017.
[2] Guillaume Bellec, Darjan Salaj, Anand Subramoney, Robert Legenstein, and Wolfgang Maass. Long short-term memory and learning-to-learn in networks of spiking neurons. Advances in Neural Information Processing Systems, 31:795–805, 2018.
[3] Sander M. Bohte, Joost N. Kok, and Han La Poutré. Error-backpropagation in temporally encoded networks of spiking neurons. Neurocomputing, 48(1-4):17-37, 2002.
[4] Olaf Booij and Hieu tat Nguyen. A gradient descent rule for spiking neurons emitting multiple spikes. Information Processing Letters, 95(6):552-558, 2005.
[5] Tong Bu, Jianhao Ding, Zhaofei Yu, and Tiejun Huang. Optimized potential initialization for low-latency spiking neural networks. In In Proceedings of the AAAI Conference on Artificial Intelligence, pages 11–20, 2022.
[6] Tong Bu, Wei Fang, Jianhao Ding, PengLin Dai, Zhaofei Yu, and Tiejun Huang. Optimal ANN-SNN conversion for high-accuracy and ultra-low-latency spiking neural networks. In International Conference on Learning Representations, 2022.
[7] Yongqiang Cao, Yang Chen, and Deepak Khosla. Spiking deep convolutional neural networks for energy-efficient object recognition. International Journal of Computer Vision, 113(1):54-66, 2015.
[8] Iulia-Maria Comsa, Krzysztof Potempa, Luca Versari, Thomas Fischbacher, Andrea Gesmundo, and Jyrki Alakuijala. Temporal coding in spiking neural networks with alpha synaptic function: Learning with backpropagation. IEEE Transactions on Neural Networks and Learning Systems, pages 1-14, 2021.
[9] Mike Davies, Narayan Srinivasa, Tsung-Han Lin, Gautham Chinya, Yongqiang Cao, Sri Harsha Choday, Georgios Dimou, Prasad Joshi, Nabil Imam, Shweta Jain, Yuyun Liao, Chit-Kwan Lin, Andrew Lines, Ruokun Liu, Deepak Mathaikutty, Steven McCoy, Arnab Paul, Jonathan Tse, Guruguhanathan Venkataramanan, Yi-Hsin Weng, Andreas Wild, Yoonseok Yang, and Hong Wang. Loihi: A neuromorphic manycore processor with on-chip learning. IEEE Micro, 38(1):82-99, 2018.
[10] Lei Deng, Yujie Wu, Xing Hu, Ling Liang, Yufei Ding, Guoqi Li, Guangshe Zhao, Peng Li, and Yuan Xie. Rethinking the performance comparison between SNNs and ANNs. Neural Networks, 121:294-307, 2020.
[11] Shikuang Deng, Yuhang Li, Shanghang Zhang, and Shi Gu. Temporal efficient training of spiking neural network via gradient re-weighting. In International Conference on Learning Representations, 2021.
[12] Jianhao Ding, Zhaofei Yu, Yonghong Tian, and Tiejun Huang. Optimal ann-snn conversion for fast and accurate inference in deep spiking neural networks. In International Joint Conference on Artificial Intelligence, pages 2328-2336, 2021.
[13] Chaoteng Duan, Jianhao Ding, Shiyan Chen, Zhaofei Yu, and Tiejun Huang. Temporal effective batch normalization in spiking neural networks. Advances in Neural Information Processing Systems, 35:34377-34390, 2022.
[14] Wei Fang, Zhaofei Yu, Yanqi Chen, Tiejun Huang, Timothee Masquelier, and Yonghong Tian. Deep residual learning in spiking neural networks. Advances in Neural Information Processing Systems, 34:21056-21069, 2021.
[15] Wei Fang, Zhaofei Yu, Yanqi Chen, Timothee Masquelier, Tiejun Huang, and Yonghong Tian. Incorporating learnable membrane time constant to enhance learning of spiking neural networks. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2661-2671, 2021.
[16] Zenke Friedemann and Ganguli Surya. Superspike: Supervised learning in multilayer spiking neural networks. Neural Computation, 30(6):1514-1541, 2018.
[17] Steve B Furber, Francesco Galluppi, Steve Temple, and Luis A Plana. The spinnaker project. Proceedings of the IEEE, 102(5):652-665, 2014.
[18] Wulfram Gerstner, Werner M Kistler, Richard Naud, and Liam Paninski. Neuronal dynamics: From single neurons to networks and models of cognition. Cambridge University Press, 2014.
[19] Yufei Guo, Xuhui Huang, and Zhe Ma. Direct learning-based deep spiking neural networks: a review. Frontiers in Neuroscience, 17:1209795, 2023.
[20] Yufei Guo, Xinyi Tong, Yuanpei Chen, Liwen Zhang, Xiaode Liu, Zhe Ma, and Xuhui Huang. Recdis-snn: Rectifying membrane potential distribution for directly training spiking neural networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 326-335, June 2022.