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Spiking neural networks (SNNs) are positioned to enable spatio-temporal information processing and ultra-low power event-driven neuromorphic hardware.
A computational model of filtering, detection, and compression in the cochlea
Richard F Lyon · 1982
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Learning representations by back-propagating errors
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams · 1986
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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
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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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BSA, a fast and accurate spike train encoding scheme
Benjamin Schrauwen and Jan Van Campenhout · 2003
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Best practices for convolutional neural networks applied to visual document analysis
Patrice Y Simard, David Steinkraus, John C Platt, et al · 2003
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A unified architecture for natural language processing: Deep neural networks with multitask learning
Ronan Collobert and Jason Weston · 2008
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Large-scale model of mammalian thalamocortical systems
Eugene M Izhikevich and Gerald M Edelman · 2008
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Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
Geoffrey Hinton, Li Deng, Dong Yu, George E Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara N Sainath, et al · 2012
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Cited alongside, same era.
Real-time classification and sensor fusion with a spiking deep belief network
Peter O’Connor, Daniel Neil, Shih-Chii Liu, Tobi Delbruck, and Michael Pfeiffer · 2013
Cited alongside, same era.
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
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Skimming digits: neuromorphic classification of spike-encoded images
Gregory K Cohen, Garrick Orchard, Sio-Hoi Leng, Jonathan Tapson, Ryad B Benosman, and André Van Schaik · 2016
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Training deep spiking neural networks using backpropagation
Jun Haeng Lee, Tobi Delbruck, and Michael Pfeiffer · 2016
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Effective sensor fusion with event-based sensors and deep network architectures
Daniel Neil and Shih-Chii Liu · 2016
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Phased lstm: Accelerating recurrent network training for long or event-based sequences
Daniel Neil, Michael Pfeiffer, and Shih-Chii Liu · 2016
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Peter O’Connor and Max Welling · 2016
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Cited alongside, same era.
The TI46 speech corpus
Mark Liberman, Robert Amsler, Ken Church, Ed Fox, Carole Hafner, Judy Klavans, Mitch Marcus, Bob Mercer, Jan Pedersen, Paul Roossin, Don Walker, Susan Warwick, and Antonio Zampolli · 2014
Cited alongside, same era.
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
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.
Spiking deep networks with lif neurons
Eric Hunsberger and Chris Eliasmith · 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.
Gregory Cohen, Saeed Afshar, Jonathan Tapson, and André van Schaik · 2017
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Event-driven random back-propagation: Enabling neuromorphic deep learning machines
Emre O Neftci, Charles Augustine, Somnath Paul, and Georgios Detorakis · 2017
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Conversion of continuous-valued deep networks to efficient event-driven networks for image classification
Bodo Rueckauer, Yuhuang Hu, Iulia-Alexandra Lungu, Michael Pfeiffer, and Shih-Chii Liu · 2017
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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 · 2017
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Superspike: Supervised learning in multilayer spiking neural networks
Friedemann Zenke and Surya Ganguli · 2018
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