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Spiking Neural Networks (SNNs) have attracted great attention due to their distinctive characteristics of low power consumption and temporal information processing.
Networks of spiking neurons: the third generation of neural network models
Wolfgang Maass · 1997
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Gradient-based learning applied to document recognition
Yann LeCun, Leon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Error-backpropagation in temporally encoded networks of spiking neurons
Sander M Bohte, Joost N Kok, and Han La Poutre · 2002
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Spiking Neuron Models: Single Neurons, Populations, Plasticity
Wulfram Gerstner and Werner M Kistler · 2002
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Imagenet: A large-scale hierarchical image database
Jia Deng, Richard Socher, Lijia Li, Kai Li, and Feifei Li · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Stochastic gradient descent tricks
Léon Bottou · 2012
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Temporal-coded deep spiking neural network with easy training and robust performance
Shibo Zhou, Xiaohua Li, Ying Chen, Sanjeev T Chandrasekaran, and Arindam Sanyal · 2012
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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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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Spiking deep convolutional neural networks for energy-efficient object recognition
Yongqiang Cao, Yang Chen, and Deepak Khosla · 2015
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Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing
Peter U Diehl, Daniel Neil, Jonathan Binas, Matthew Cook, Shih-Chii Liu, and Michael Pfeiffer · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Improved regularization of convolutional neural networks with cutout
Terrance DeVries and Graham W Taylor · 2017
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SGDR: stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2017
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Supervised learning based on temporal coding in spiking neural networks
Hesham Mostafa · 2017
Cited alongside, same era.
Conversion of continuous-valued deep networks to efficient event-driven networks for image classification
Bodo Rueckauer, Iulia-Alexandra Lungu, Yuhuang Hu, Michael Pfeiffer, and Shih-Chii Liu · 2017
Cited alongside, same era.
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
Cited alongside, same era.
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
Cited alongside, same era.
Temporally efficient deep learning with spikes
Peter O’Connor, Efstratios Gavves, Matthias Reisser, and Max Welling · 2018
Cited alongside, same era.
Enabling deep spiking neural networks with hybrid conversion and spike timing dependent backpropagation
Nitin Rathi, Gopalakrishnan Srinivasan, Priyadarshini Panda, 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
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Optimal conversion of conventional artificial neural networks to spiking neural networks
Shikuang Deng and Shi Gu · 2021
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Optimal ANN-SNN conversion for fast and accurate inference in deep spiking neural networks
Jianhao Ding, Zhaofei Yu, Yonghong Tian, and Tiejun Huang · 2021
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Incorporating learnable membrane time constant to enhance learning of spiking neural networks
Wei Fang, Zhaofei Yu, Yanqi Chen, Timothee Masquelier, Tiejun Huang, and Yonghong Tian · 2021
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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
Cited alongside, same era.
Superspike: Supervised learning in multilayer spiking neural networks
Friedemann Zenke and Surya Ganguli · 2018
Cited alongside, same era.
Autoaugment: Learning augmentation strategies from data
Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le · 2019
Cited alongside, same era.
TrueNorth: Accelerating from zero to 64 million neurons in 10 years
Michael V DeBole, Brian Taba, Arnon Amir, Filipp Akopyan, Alexander Andreopoulos, William P Risk, Jeff Kusnitz, Carlos Ortega Otero, Tapan K Nayak, Rathinakumar Appuswamy, et al · 2019
Cited alongside, same era.
Going deeper in spiking neural networks: VGG and residual architectures
Abhronil Sengupta, Yuting Ye, Robert Wang, Chiao Liu, and Kaushik Roy · 2019
Cited alongside, same era.
Direct training for spiking neural networks: Faster, larger, better
Yujie Wu, Lei Deng, Guoqi Li, Jun Zhu, Yuan Xie, and Luping Shi · 2019
Cited alongside, same era.
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
Cited alongside, same era.
TCL: an ANN-to-SNN conversion with trainable clipping layers
Nguyen-Dong Ho and Ik-Joon Chang · 2021
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A free lunch from ANN: Towards efficient, accurate spiking neural networks calibration
Yuhang Li, Shikuang Deng, Xin Dong, Ruihao Gong, and Shi Gu · 2021
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DIET-SNN: A low-latency spiking neural network with direct input encoding and leakage and threshold optimization
Nitin Rathi and Kaushik Roy · 2021
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The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks
Friedemann Zenke and Tim P Vogels · 2021
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Going deeper with directly-trained larger spiking neural networks
Hanle Zheng, Yujie Wu, Lei Deng, Yifan Hu, and Guoqi Li · 2021
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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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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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Quantization framework for fast spiking neural networks
Chen Li, Lei Ma, and Steve Furber · 2022
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Efficient and accurate conversion of spiking neural network with burst spikes
Yang Li and Yi Zeng · 2022
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