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Spiking neural networks have gained significant attention due to their brain-like information processing capabilities.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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The neuronal encoding of information in the brain
Edmund T Rolls and Alessandro Treves · 2011
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Principles of neural coding
Rodrigo Quian Quiroga and Stefano Panzeri · 2013
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Empirical evaluation of rectified activations in convolutional network
Bing Xu, Naiyan Wang, Tianqi Chen, and Mu Li · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Converting static image datasets to spiking neuromorphic datasets using saccades
Garrick Orchard, Ajinkya Jayawant, Gregory K Cohen, and Nitish Thakor · 2015
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Cifar10-dvs: an event-stream dataset for object classification
Hongmin Li, Hanchao Liu, Xiangyang Ji, Guoqi Li, and Luping Shi · 2017
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Code under construction: neural coding over development
Lilach Avitan and Geoffrey J Goodhill · 2018
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Towards spike-based machine intelligence with neuromorphic computing
Kaushik Roy, Akhilesh Jaiswal, and Priyadarshini Panda · 2019
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Direct training for spiking neural networks: Faster, larger, better
Yujie Wu, Lei Deng, Guoqi Li, Jun Zhu, Yuan Xie, and Luping Shi · 2019
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Enabling deep spiking neural networks with hybrid conversion and spike timing dependent backpropagation
Nitin Rathi, Gopalakrishnan Srinivasan, Priyadarshini Panda, and Kaushik Roy · 2019
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Dart: distribution aware retinal transform for event-based cameras
Bharath Ramesh, Hong Yang, Garrick Orchard, Ngoc Anh Le Thi, Shihao Zhang, and Cheng Xiang · 2019
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Rmp-snn: Residual membrane potential neuron for enabling deeper high-accuracy and low-latency spiking neural network
Bing Han, Gopalakrishnan Srinivasan, and Kaushik Roy · 2020
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Lisnn: Improving spiking neural networks with lateral interactions for robust object recognition
Xiang Cheng, Yunzhe Hao, Jiaming Xu, and Bo Xu · 2020
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Nitin Rathi and Kaushik Roy · 2020
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Temporal spike sequence learning via backpropagation for deep spiking neural networks
Wenrui Zhang and Peng Li · 2020
Cited alongside, same era.
Efficient processing of spatio-temporal data streams with spiking neural networks
Alexander Kugele, Thomas Pfeil, Michael Pfeiffer, and Elisabetta Chicca · 2020
Cited alongside, same era.
High-speed image reconstruction through short-term plasticity for spiking cameras
Yajing Zheng, Lingxiao Zheng, Zhaofei Yu, Boxin Shi, Yonghong Tian, and Tiejun Huang · 2021
Cited alongside, same era.
Differentiable spike: Rethinking gradient-descent for training spiking neural networks
Yuhang Li, Yufei Guo, Shanghang Zhang, Shikuang Deng, Yongqing Hai, and Shi Gu · 2021
Cited alongside, same era.
Incorporating learnable membrane time constant to enhance learning of spiking neural networks
Wei Fang, Zhaofei Yu, Yanqi Chen, Timothée Masquelier, Tiejun Huang, and Yonghong Tian · 2021
Cited alongside, same era.
Im-loss: information maximization loss for spiking neural networks
Yufei Guo, Yuanpei Chen, Liwen Zhang, Xiaode Liu, Yinglei Wang, Xuhui Huang, and Zhe Ma · 2022
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Temporal efficient training of spiking neural network via gradient re-weighting
Shikuang Deng, Yuhang Li, Shanghang Zhang, and Shi Gu · 2022
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Temporal effective batch normalization in spiking neural networks
Chaoteng Duan, Jianhao Ding, Shiyan Chen, Zhaofei Yu, and Tiejun Huang · 2022
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Ltmd: Learning improvement of spiking neural networks with learnable thresholding neurons and moderate dropout
Siqi Wang, Tee Hiang Cheng, and Meng-Hiot Lim · 2022
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Tcja-snn: Temporal-channel joint attention for spiking neural networks
Rui-Jie Zhu, Qihang Zhao, Tianjing Zhang, Haoyu Deng, Yule Duan, Malu Zhang, and Liang-Jian Deng · 2022
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Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks
Bojian Yin, Federico Corradi, and Sander M Bohté · 2021
Cited alongside, same era.
Going deeper with directly-trained larger spiking neural networks
Hanle Zheng, Yujie Wu, Lei Deng, Yifan Hu, and Guoqi Li · 2021
Cited alongside, same era.
Temporal-wise attention spiking neural networks for event streams classification
Man Yao, Huanhuan Gao, Guangshe Zhao, Dingheng Wang, Yihan Lin, Zhaoxu Yang, and Guoqi Li · 2021
Cited alongside, same era.
Deep residual learning in spiking neural networks
Wei Fang, Zhaofei Yu, Yanqi Chen, Tiejun Huang, Timothée Masquelier, and Yonghong Tian · 2021
Cited alongside, same era.
Optimal ann-snn conversion for high-accuracy and ultra-low-latency spiking neural networks
Tong Bu, Wei Fang, Jianhao Ding, PengLin Dai, Zhaofei Yu, and Tiejun Huang · 2021
Cited alongside, same era.
Liaf-net: Leaky integrate and analog fire network for lightweight and efficient spatiotemporal information processing
Zhenzhi Wu, Hehui Zhang, Yihan Lin, Guoqi Li, Meng Wang, and Ye Tang · 2021
Cited alongside, same era.
Yi Zeng, Dongcheng Zhao, Feifei Zhao, Guobin Shen, Yiting Dong, Enmeng Lu, Qian Zhang, Yinqian Sun, Qian Liang, Yuxuan Zhao, et al · 2022
Cited alongside, same era.
Later among the works it cites.
Backeisnn: A deep spiking neural network with adaptive self-feedback and balanced excitatory–inhibitory neurons
Dongcheng Zhao, Yi Zeng, and Yang Li · 2022
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Spikformer: When spiking neural network meets transformer
Zhaokun Zhou, Yuesheng Zhu, Chao He, Yaowei Wang, Shuicheng Yan, Yonghong Tian, and Li Yuan · 2022
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Autosnn: towards energy-efficient spiking neural networks
Byunggook Na, Jisoo Mok, Seongsik Park, Dongjin Lee, Hyeokjun Choe, and Sungroh Yoon · 2022
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Neural architecture search for spiking neural networks
Youngeun Kim, Yuhang Li, Hyoungseob Park, Yeshwanth Venkatesha, and Priyadarshini Panda · 2022
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Backpropagation with biologically plausible spatiotemporal adjustment for training deep spiking neural networks
Guobin Shen, Dongcheng Zhao, and Yi Zeng · 2022
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Recdis-snn: Rectifying membrane potential distribution for directly training spiking neural networks
Yufei Guo, Xinyi Tong, Yuanpei Chen, Liwen Zhang, Xiaode Liu, Zhe Ma, and Xuhui Huang · 2022
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Neuromorphic data augmentation for training spiking neural networks
Yuhang Li, Youngeun Kim, Hyoungseob Park, Tamar Geller, and Priyadarshini Panda · 2022
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Eventmix: An efficient augmentation strategy for event-based data
Guobin Shen, Dongcheng Zhao, and Yi Zeng · 2022
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Attention spiking neural networks
Man Yao, Guangshe Zhao, Hengyu Zhang, Yifan Hu, Lei Deng, Yonghong Tian, Bo Xu, and Guoqi Li · 2023
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Temporal knowledge sharing enable spiking neural network learning from past and future
Yiting Dong, Dongcheng Zhao, and Yi Zeng · 2023
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