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Spiking neural networks (SNNs) well support spatiotemporal learning and energy-efficient event-driven hardware neuromorphic processors.
A computational model of filtering, detection, and compression in the cochlea
Richard Lyon · 1982
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Backpropagation through time: what it does and how to do it
Paul J Werbos · 1990
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TI 46-word LDC93S9, 1991
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 · 1991
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Tidigits speech corpus
R Gary Leonard and George Doddington · 1993
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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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Real-time computing without stable states: A new framework for neural computation based on perturbations
Wolfgang Maass, Thomas Natschläger, and Henry Markram · 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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Neuro-inspired speech recognition with recurrent spiking neurons
Arfan Ghani, T Martin McGinnity, Liam P Maguire, and Jim Harkin · 2008
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Large-scale model of mammalian thalamocortical systems
Eugene M Izhikevich and Gerald M Edelman · 2008
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Phenomenological models of synaptic plasticity based on spike timing
Abigail Morrison, Markus Diesmann, and Wulfram Gerstner · 2008
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Supervised learning in spiking neural networks with resume: sequence learning, classification, and spike shifting
Filip Ponulak and Andrzej Kasiński · 2010
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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
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Deep neural networks for object detection
Christian Szegedy, Alexander Toshev, and Dumitru Erhan · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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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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Training deep spiking neural networks using backpropagation
Jun Haeng Lee, Tobi Delbruck, and Michael Pfeiffer · 2016
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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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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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Deep learning using rectified linear units (relu)
Abien Fred Agarap · 2018
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Feature representations for neuromorphic audio spike streams
Jithendar Anumula, Daniel Neil, Tobi Delbruck, and Shih-Chii Liu · 2018
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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.
Unsupervised learning of digit recognition using spike-timing-dependent plasticity
Peter U Diehl and Matthew Cook · 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.
Spiking deep networks with lif neurons
Eric Hunsberger and Chris Eliasmith · 2015
Cited alongside, same era.
Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
Cited alongside, same era.
A digital liquid state machine with biologically inspired learning and its application to speech recognition
Yong Zhang, Peng Li, Yingyezhe Jin, and Yoonsuck Choe · 2015
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
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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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Gradient descent for spiking neural networks
Dongsung Huh and Terrence J Sejnowski · 2018
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Hybrid macro/micro level backpropagation for training deep spiking neural networks
Yingyezhe Jin, Wenrui Zhang, and Peng Li · 2018
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Biologically motivated algorithms for propagating local target representations
Alexander G Ororbia and Ankur Mali · 2018
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Slayer: Spike layer error reassignment in time
Sumit Bam Shrestha and Garrick Orchard · 2018
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Spilinc: Spiking liquid-ensemble computing for unsupervised speech and image recognition
Gopalakrishnan Srinivasan, Priyadarshini Panda, and Kaushik Roy · 2018
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Analysis of liquid ensembles for enhancing the performance and accuracy of liquid state machines
Parami Wijesinghe, Gopalakrishnan Srinivasan, Priyadarshini Panda, and Kaushik Roy · 2019
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