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Spiking neural networks (SNNs) are well suited for spatio-temporal learning and implementations on energy-efficient event-driven neuromorphic processors.
Surrogate gradient learning in spiking neural networks, 2019
EO Neftci, H Mostafa, and F Zenke · 1901
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Backpropagation through time: what it does and how to do it
Paul J Werbos · 1990
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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, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Error-backpropagation in temporally encoded networks of spiking neurons
Sander M Bohte, Joost N Kok, and Han La Poutre · 2002
Earlier work this paper cites.
Spiking neuron models: Single neurons, populations, plasticity
Wulfram Gerstner and Werner M Kistler · 2002
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A gradient descent rule for spiking neurons emitting multiple spikes
Olaf Booij and Hieu tat Nguyen · 2005
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A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection
Samanwoy Ghosh-Dastidar and Hojjat Adeli · 2009
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A supervised multi-spike learning algorithm based on gradient descent for spiking neural networks
Yan Xu, Xiaoqin Zeng, Lixin Han, and Jing Yang · 2013
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The cifar-10 dataset
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton · 2014
Earlier work this paper cites.
Truenorth: Design and tool flow of a 65 mw 1 million neuron programmable neurosynaptic chip
Filipp Akopyan, Jun Sawada, Andrew Cassidy, Rodrigo Alvarez-Icaza, John Arthur, Paul Merolla, Nabil Imam, Yutaka Nakamura, Pallab Datta, Gi-Joon Nam, et al · 2015
Cited alongside, same era.
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
Cited alongside, same era.
Backpropagation for energy-efficient neuromorphic computing
Steve K Esser, Rathinakumar Appuswamy, Paul Merolla, John V Arthur, and Dharmendra S Modha · 2015
Cited alongside, same era.
Converting static image datasets to spiking neuromorphic datasets using saccades
Garrick Orchard, Ajinkya Jayawant, Gregory K Cohen, and Nitish Thakor · 2015
Cited alongside, same era.
Training spiking deep networks for neuromorphic hardware
Eric Hunsberger and Chris Eliasmith · 2016
Cited alongside, same era.
Hybrid macro/micro level backpropagation for training deep spiking neural networks
Yingyezhe Jin, Wenrui Zhang, and Peng Li · 2018
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Slayer: Spike layer error reassignment in time
Sumit Bam Shrestha and Garrick Orchard · 2018
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Deep learning in spiking neural networks
Amirhossein Tavanaei, Masoud Ghodrati, Saeed Reza Kheradpisheh, Timothée Masquelier, and Anthony Maida · 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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Superspike: Supervised learning in multilayer spiking neural networks
Friedemann Zenke and Surya Ganguli · 2018
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Training deep spiking neural networks using backpropagation
Jun Haeng Lee, Tobi Delbruck, and Michael Pfeiffer · 2016
Cited alongside, same era.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 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.
Gradient descent for spiking neural networks
Dongsung Huh and Terrence J Sejnowski · 2018
Cited alongside, same era.
Guillaume Bellec, Franz Scherr, Elias Hajek, Darjan Salaj, Robert Legenstein, and Wolfgang Maass · 2019
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Going deeper in spiking neural networks: Vgg and residual architectures
Abhronil Sengupta, Yuting Ye, Robert Wang, Chiao Liu, and Kaushik Roy · 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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Spike-train level backpropagation for training deep recurrent spiking neural networks
Wenrui Zhang and Peng Li · 2019
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