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Spiking Neural Networks (SNNs) provide an energy-efficient deep learning option due to their unique spike-based event-driven (i.e., spike-driven) paradigm.
A quantitative description of membrane current and its application to conduction and excitation in nerve
A. L. Hodgkin and A. F. Huxley · 1952
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Networks of spiking neurons: the third generation of neural network models
Wolfgang Maass · 1997
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Simple model of spiking neurons
Eugene M Izhikevich · 2003
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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A wafer-scale neuromorphic hardware system for large-scale neural modeling
Johannes Schemmel, Daniel Brüderle, Andreas Grübl, Matthias Hock, Karlheinz Meier, and Sebastian Millner · 2010
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Spinnaker: A 1-w 18-core system-on-chip for massively-parallel neural network simulation
Eustace Painkras, Luis A Plana, Jim Garside, Steve Temple, Francesco Galluppi, Cameron Patterson, David R Lester, Andrew D Brown, and Steve B Furber · 2013
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A million spiking-neuron integrated circuit with a scalable communication network and interface
Paul A Merolla, John V Arthur, Rodrigo Alvarez-Icaza, Andrew S Cassidy, Jun Sawada, Filipp Akopyan, Bryan L Jackson, Nabil Imam, Chen Guo, Yutaka Nakamura, et al · 2014
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Neurogrid: A mixed-analog-digital multichip system for large-scale neural simulations
Ben Varkey Benjamin, Peiran Gao, Emmett McQuinn, Swadesh Choudhary, Anand R Chandrasekaran, Jean-Marie Bussat, Rodrigo Alvarez-Icaza, John V Arthur, Paul A Merolla, and Kwabena Boahen · 2014
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1.1 computing’s energy problem (and what we can do about it)
Mark Horowitz · 2014
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Darwin: A neuromorphic hardware co-processor based on spiking neural networks
Juncheng Shen, De Ma, Zonghua Gu, Ming Zhang, Xiaolei Zhu, Xiaoqiang Xu, Qi Xu, Yangjing Shen, and Gang Pan · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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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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A low power, fully event-based gesture recognition system
Arnon Amir, Brian Taba, David Berg, Timothy Melano, Jeffrey McKinstry, Carmelo Di Nolfo, Tapan Nayak, Alexander Andreopoulos, Guillaume Garreau, Marcela Mendoza, Jeff Kusnitz, Michael Debole, Steve Esser, Tobi Delbruck, Myron Flickner, and Dharmendra Modha · 2017
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Pruning convolutional neural networks for resource efficient inference
Pavlo Molchanov, Stephen Tyree, Tero Karras, Timo Aila, and Jan Kautz · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
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Loihi: A neuromorphic manycore processor with on-chip learning
Mike Davies, Narayan Srinivasa, Tsung-Han Lin, Gautham Chinya, Yongqiang Cao, Sri Harsha Choday, Georgios Dimou, Prasad Joshi, Nabil Imam, Shweta Jain, et al · 2018
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Long short-term memory and learning-to-learn in networks of spiking neurons
Guillaume Bellec, Darjan Salaj, Anand Subramoney, Robert Legenstein, and Wolfgang Maass · 2018
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Spatio-temporal backpropagation for training high-performance spiking neural networks
Yujie Wu, Lei Deng, Guoqi Li, Jun Zhu, and Luping Shi · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Dynamical isometry and a mean field theory of cnns: How to train 10,000-layer vanilla convolutional neural networks
Lechao Xiao, Yasaman Bahri, Jascha Sohl-Dickstein, Samuel Schoenholz, and Jeffrey Pennington · 2018
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Squeeze-and-excitation networks
Jie Hu, Li Shen, and Gang Sun · 2018
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Cbam: Convolutional block attention module
Sanghyun Woo, Jongchan Park, Joon-Young Lee, and In So Kweon · 2018
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Towards artificial general intelligence with hybrid tianjic chip architecture
Jing Pei, Lei Deng, et al · 2019
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Towards spike-based machine intelligence with neuromorphic computing
Kaushik Roy, Akhilesh Jaiswal, and Priyadarshini Panda · 2019
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Surrogate gradient learning in spiking neural networks: Bringing the power of gradient-based optimization to spiking neural networks
Emre O Neftci, Hesham Mostafa, and Friedemann Zenke · 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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Are sixteen heads really better than one?
Paul Michel, Omer Levy, and Graham Neubig · 2019
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Spike-Triggered Non-Autoregressive Transformer for End-to-End Speech Recognition
Zhengkun Tian, Jiangyan Yi, Jianhua Tao, Ye Bai, Shuai Zhang, and Zhengqi Wen · 2020
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Transformers are rnns: Fast autoregressive transformers with linear attention
A. Katharopoulos, A. Vyas, N. Pappas, and F. Fleuret · 2020
Cited alongside, same era.
Event-driven spiking convolutional neural network
Ole Richer, Ning Qiao, Qian Liu, and Sadique Sheik · 2020
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
Cited alongside, same era.
Nitin Rathi, Gopalakrishnan Srinivasan, Priyadarshini Panda, and Kaushik Roy · 2020
Cited alongside, same era.
Toward scalable, efficient, and accurate deep spiking neural networks with backward residual connections, stochastic softmax, and hybridization
Priyadarshini Panda, Sai Aparna Aketi, and Kaushik Roy · 2020
Spikeformer: A novel architecture for training high-performance low-latency spiking neural network
Yudong Li, Yunlin Lei, and Xu Yang · 2022
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Spiking transformers for event-based single object tracking
Jiqing Zhang, Bo Dong, Haiwei Zhang, Jianchuan Ding, Felix Heide, Baocai Yin, and Xin Yang · 2022
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Spike transformer: Monocular depth estimation for spiking camera
Jiyuan Zhang, Lulu Tang, Zhaofei Yu, Jiwen Lu, and Tiejun Huang · 2022
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Efficient transformers: A survey
Yi Tay, Mostafa Dehghani, Dara Bahri, and Donald Metzler · 2022
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Brain-inspired global-local learning incorporated with neuromorphic computing
Yujie Wu, Rong Zhao, Jun Zhu, Feng Chen, Mingkun Xu, Guoqi Li, Sen Song, Lei Deng, Guanrui Wang, Hao Zheng, et al · 2022
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Cited alongside, same era.
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.
Spiking transformer networks: A rate coded approach for processing sequential data
Etienne Mueller, Viktor Studenyak, Daniel Auge, and Alois Knoll · 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.
Advancing spiking neural networks towards deep residual learning
Yifan Hu, Lei Deng, Yujie Wu, Man Yao, and Guoqi Li · 2021
Cited alongside, same era.
Self-backpropagation of synaptic modifications elevates the efficiency of spiking and artificial neural networks
Tielin Zhang, Xiang Cheng, Shuncheng Jia, Mu-ming Poo, Yi Zeng, and Bo Xu · 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.
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.
A long short-term memory for ai applications in spike-based neuromorphic hardware
Arjun Rao, Philipp Plank, Andreas Wild, and Wolfgang Maass · 2022
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Spiking neural network integrated circuits: A review of trends and future directions
Arindam Basu, Lei Deng, Charlotte Frenkel, and Xueyong Zhang · 2022
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Efficientformer: Vision transformers at mobilenet speed
Yanyu Li, Geng Yuan, Yang Wen, Ju Hu, Georgios Evangelidis, Sergey Tulyakov, Yanzhi Wang, and Jian Ren · 2022
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Parc-net: Position aware circular convolution with merits from convnets and transformer
Haokui Zhang, Wenze Hu, and Xiaoyu Wang · 2022
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cosformer: Rethinking softmax in attention
Zhen Qin, Weixuan Sun, Hui Deng, Dongxu Li, Yunshen Wei, Baohong Lv, Junjie Yan, Lingpeng Kong, and Yiran Zhong · 2022
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GLIF: A unified gated leaky integrate-and-fire neuron for spiking neural networks
Xingting Yao, Fanrong Li, Zitao Mo, and Jian Cheng · 2022
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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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A comprehensive and modularized statistical framework for gradient norm equality in deep neural networks
Zhaodong Chen, Lei Deng, Bangyan Wang, Guoqi Li, and Yuan Xie · 2022
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Deepnet: Scaling transformers to 1,000 layers
Hongyu Wang, Shuming Ma, Li Dong, Shaohan Huang, Dongdong Zhang, and Furu Wei · 2022
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Attention mechanisms in computer vision: A survey
Meng-Hao Guo, Tian-Xing Xu, Jiang-Jiang Liu, Zheng-Ning Liu, Peng-Tao Jiang, Tai-Jiang Mu, Song-Hai Zhang, Ralph R Martin, Ming-Ming Cheng, and Shi-Min Hu · 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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Training high-performance low-latency spiking neural networks by differentiation on spike representation
Qingyan Meng, Mingqing Xiao, Shen Yan, Yisen Wang, Zhouchen Lin, and Zhi-Quan Luo · 2022
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Event-based vision: A survey
Guillermo Gallego, Tobi Delbrück, Garrick Orchard, Chiara Bartolozzi, Brian Taba, Andrea Censi, Stefan Leutenegger, Andrew J. Davison, Jörg Conradt, Kostas Daniilidis, and Davide Scaramuzza · 2022
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Online transformers with spiking neurons for fast prosthetic hand control
Nathan Leroux, Jan Finkbeiner, and Emre Neftci · 2023
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Spikegpt: Generative pre-trained language model with spiking neural networks
Rui-Jie Zhu, Qihang Zhao, and Jason K Eshraghian · 2023
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Complex dynamic neurons improved spiking transformer network for efficient automatic speech recognition
Minglun Han, Qingyu Wang, Tielin Zhang, Yi Wang, Duzhen Zhang, and Bo Xu · 2023
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Event-based human pose tracking by spiking spatiotemporal transformer
Shihao Zou, Yuxuan Mu, Xinxin Zuo, Sen Wang, and Li Cheng · 2023
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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 · 2023
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Training full spike neural networks via auxiliary accumulation pathway
Guangyao Chen, Peixi Peng, Guoqi Li, and Yonghong Tian · 2023
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Spikingformer: Spike-driven residual learning for transformer-based spiking neural network
Chenlin Zhou, Liutao Yu, Zhaokun Zhou, Han Zhang, Zhengyu Ma, Huihui Zhou, and Yonghong Tian · 2023
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Attention spiking neural networks
Man Yao, Guangshe Zhao, Hengyu Zhang, Hu Yifan, Lei Deng, Yonghong Tian, Bo Xu, and Guoqi Li · 2023
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Brain inspired computing: A systematic survey and future trends
Guoqi Li, Lei Deng, Huajin Tang, Gang Pan, Yonghong Tian, Kaushik Roy, and Wolfgang Maass · 2023
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Probabilistic modeling: Proving the lottery ticket hypothesis in spiking neural network
Man Yao, Yuhong Chou, Guangshe Zhao, Xiawu Zheng, Yonghong Tian, Bo Xu, and Guoqi Li · 2023
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Dynamic spatial sparsification for efficient vision transformers and convolutional neural networks
Yongming Rao, Zuyan Liu, Wenliang Zhao, Jie Zhou, and Jiwen Lu · 2023
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Hydra attention: Efficient attention with many heads
Daniel Bolya, Cheng-Yang Fu, Xiaoliang Dai, Peizhao Zhang, and Judy Hoffman · 2023
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Swiftformer: Efficient additive attention for transformer-based real-time mobile vision applications
Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, and Fahad Shahbaz Khan · 2023
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