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Spiking neural networks have shown much promise as an energy-efficient alternative to artificial neural networks.
A logical calculus of the ideas immanent in nervous activity
Warren S McCulloch and Walter Pitts · 1943
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Backpropagation through time: what it does and how to do it
Paul J Werbos · 1990
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Phase relationship between hippocampal place units and the eeg theta rhythm
John O’Keefe and Michael L Recce · 1993
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Networks of spiking neurons: the third generation of neural network models
Wolfgang Maass · 1997
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Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type
Guo-qiang Bi and Mu-ming Poo · 1998
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Spikeprop: backpropagation for networks of spiking neurons
Sander M Bohte, Joost N Kok, and Johannes A La Poutré · 2000
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Spiking neuron models: Single neurons, populations, plasticity
Wulfram Gerstner and Werner M Kistler · 2002
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Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
Li Fei-Fei, Rob Fergus, and Pietro Perona · 2004
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The organization of behavior: A neuropsychological theory
Donald Olding Hebb · 2005
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The tempotron: a neuron that learns spike timing–based decisions
Robert Gütig and Haim Sompolinsky · 2006
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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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Cats and dogs
Omkar M. Parkhi, Andrea Vedaldi, Andrew Zisserman, and C. V. Jawahar · 2012
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Span: Spike pattern association neuron for learning spatio-temporal spike patterns
Ammar Mohemmed, Stefan Schliebs, Satoshi Matsuda, and Nikola Kasabov · 2012
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Parametric blur estimation for blind restoration of natural images: Linear motion and out-of-focus
Joao P Oliveira, Mario AT Figueiredo, and Jose M Bioucas-Dias · 2013
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The spinnaker project
Steve B Furber, Francesco Galluppi, Steve Temple, and Luis A Plana · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Computing’s energy problem (and what we can do about it). in 2014 ieee international solid-state circuits conference digest of technical papers (isscc)
Mark Horowitz · 2014
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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
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Converting static image datasets to spiking neuromorphic datasets using saccades
Garrick Orchard, Ajinkya Jayawant, Gregory K Cohen, and Nitish Thakor · 2015
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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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Spiking deep networks with lif neurons
Eric Hunsberger and Chris Eliasmith · 2015
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Training spiking deep networks for neuromorphic hardware
Eric Hunsberger and Chris Eliasmith · 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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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, et al · 2017
Cited alongside, same era.
Cifar10-dvs: an event-stream dataset for object classification
Hongmin Li, Hanchao Liu, Xiangyang Ji, Guoqi Li, and Luping Shi · 2017
Cited alongside, same era.
Cyclical learning rates for training neural networks
Leslie N Smith · 2017
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Efficient processing of deep neural networks: A tutorial and survey
Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang, and Joel S Emer · 2017
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.
Object detection based on lidar temporal pulses using spiking neural networks
Distance-iou loss: Faster and better learning for bounding box regression
Zhaohui Zheng, Ping Wang, Wei Liu, Jinze Li, Rongguang Ye, and Dongwei Ren · 2020
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Event density based denoising method for dynamic vision sensor
Yang Feng, Hengyi Lv, Hailong Liu, Yisa Zhang, Yuyao Xiao, and Chengshan Han · 2020
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Array programming with numpy
Charles R Harris, K Jarrod Millman, Stéfan J Van Der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J Smith, et al · 2020
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Unraveling the paradox of intensity-dependent dvs pixel noise
Rui Graca and Tobi Delbruck · 2021
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Tonic: event-based datasets and transformations., July 2021
Gregor Lenz, Kenneth Chaney, Sumit Bam Shrestha, Omar Oubari, Serge Picaud, and Guido Zarrella · 2021
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Shibo Zhou and Wei Wang · 2018
Cited alongside, same era.
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.
Deep neural networks with weighted spikes
Jaehyun Kim, Heesu Kim, Subin Huh, Jinho Lee, and Kiyoung Choi · 2018
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.
Improving spiking neural networks trained with spike timing dependent plasticity for image recognition
Pierre Falez · 2019
Cited alongside, same era.
Feature extraction using spiking convolutional neural networks
Ruthvik Vaila, John Chiasson, and Vishal Saxena · 2019
Cited alongside, same era.
Multi-layered spiking neural network with target timestamp threshold adaptation and stdp
Pierre Falez, Pierre Tirilly, Ioan Marius Bilasco, Philippe Devienne, and Pierre Boulet · 2019
Cited alongside, same era.
Neural coding in spiking neural networks: A comparative study for robust neuromorphic systems
Wenzhe Guo, Mohammed E Fouda, Ahmed M Eltawil, and Khaled Nabil Salama · 2021
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A study on the effects of pre-processing on spatio-temporal action recognition using spiking neural networks trained with stdp
Mireille El-Assal, Pierre Tirilly, and Ioan Marius Bilasco · 2021
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Beyond classification: Directly training spiking neural networks for semantic segmentation
Youngeun Kim, Joshua Chough, and Priyadarshini Panda · 2021
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Advancing neuromorphic computing with loihi: A survey of results and outlook
Mike Davies, Andreas Wild, Garrick Orchard, Yulia Sandamirskaya, Gabriel A Fonseca Guerra, Prasad Joshi, Philipp Plank, and Sumedh R Risbud · 2021
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Training spiking neural networks using lessons from deep learning
Jason K Eshraghian, Max Ward, Emre Neftci, Xinxin Wang, Gregor Lenz, Girish Dwivedi, Mohammed Bennamoun, Doo Seok Jeong, and Wei D Lu · 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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Stereospike: Depth learning with a spiking neural network
Ulysse Rançon, Javier Cuadrado-Anibarro, Benoit R Cottereau, and Timothée Masquelier · 2021
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The backpropagation algorithm implemented on spiking neuromorphic hardware
Alpha Renner, Forrest Sheldon, Anatoly Zlotnik, Louis Tao, and Andrew Sornborger · 2021
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Incorporating learnable membrane time constant to enhance learning of spiking neural networks
Wei Fang, Zhaofei Yu, Yanqi Chen, Timothée Masquelier, Tiejun Huang, and Yonghong Tian · 2021
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Deep residual learning in spiking neural networks
Wei Fang, Zhaofei Yu, Yanqi Chen, Tiejun Huang, Timothée Masquelier, and Yonghong Tian · 2021
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Deep spiking convolutional neural network for single object localization based on deep continuous local learning
Sami Barchid, José Mennesson, and Chaabane Djéraba · 2021
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Is neuromorphic mnist neuromorphic? analyzing the discriminative power of neuromorphic datasets in the time domain
Laxmi R Iyer, Yansong Chua, and Haizhou Li · 2021
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v2e: From video frames to realistic dvs events
Yuhuang Hu, Shih-Chii Liu, and Tobi Delbruck · 2021
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Low-activity supervised convolutional spiking neural networks applied to speech commands recognition
Thomas Pellegrini, Romain Zimmer, and Timothée Masquelier · 2021
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2022 roadmap on neuromorphic computing and engineering
Dennis Valbjørn Christensen, Regina Dittmann, Bernabe Linares-Barranco, Abu Sebastian, Manuel Le Gallo, Andrea Redaelli, Stefan Slesazeck, Thomas Mikolajick, Sabina Spiga, Stephan Menzel, Ilia Valov, Gianluca Milano, Carlo Ricciardi, Shi-Jun Liang, Feng Miao, Mario Lanza, Tyler J. Quill, Scott Tom Keene, Alberto Salleo, Julie Grollier, Danijela Markovic, Alice Mizrahi, Peng Yao, J. Joshua Yang, Giacomo Indiveri, John Paul Strachan, Suman Datta, Elisa Vianello, Alexandre Valentian, Johannes Feldmann, Xuan Li, Wolfram HP Pernice, Harish Bhaskaran, Steve Furber, Emre Neftci, Franz Scherr, Wolfgang Maass, Srikanth Ramaswamy, Jonathan Tapson, Priyadarshini Panda, Youngeun Kim, Gouhei Tanaka, Simon Thorpe, Chiara Bartolozzi, Thomas A Cleland, Christoph Posch, Shih-Chii Liu, Gabriella Panuccio, Mufti Mahmud, Arnab Neelim Mazumder, Morteza Hosseini, Tinoosh Mohsenin, Elisa Donati, Silvia Tolu, Roberto Galeazzi, Martin Ejsing Christensen, Sune Holm, Daniele Ielmini, and Nini Pryds · 2022
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Reckon: A 28nm sub-mm 2 task-agnostic spiking recurrent neural network processor enabling on-chip learning over second-long timescales
Charotte Frenkel and Giacomo Indiveri · 2022
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Spikingjelly
Wei Fang, Yanqi Chen, Jianhao Ding, Ding Chen, Zhaofei Yu, Huihui Zhou, Yonghong Tian, and other contributors · 2022
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