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Spiking Neural Networks (SNNs) have attracted enormous research interest due to temporal information processing capability, low power consumption, and high biological plausibility.
A brief history of time (constants)
Christof Koch, Moshe Rapp, and Idan Segev · 1996
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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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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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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Population dynamics of interacting spiking neurons
Maurizio Mattia and Paolo Del Giudice · 2002
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Learning in spiking neural networks by reinforcement of stochastic synaptic transmission
H Sebastian Seung · 2003
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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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Mechanisms underlying working memory for novel information
Michael E Hasselmo and Chantal E Stern · 2006
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Fast and adaptive network of spiking neurons for multi-view visual pattern recognition
Simei Gomes Wysoski, Lubica Benuskova, and Nikola Kasabov · 2006
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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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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Spatio-temporal credit assignment in neuronal population learning
Johannes Friedrich, Robert Urbanczik, and Walter Senn · 2011
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An imperfect dopaminergic error signal can drive temporal-difference learning
Wiebke Potjans, Markus Diesmann, and Abigail Morrison · 2011
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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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A scale-invariant internal representation of time
Karthik H Shankar and Marc W Howard · 2012
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Self-organizing spiking neural model for learning fault-tolerant spatio-motor transformations
Narayan Srinivasa and Youngkwan Cho · 2012
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Pattern recognition computation in a spiking neural network with temporal encoding and learning
Qiang Yu, Kay Chen Tan, and Huajin Tang · 2012
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Reinforcement learning using a continuous time actor-critic framework with spiking neurons
Nicolas Frémaux, Henning Sprekeler, and Wulfram Gerstner · 2013
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Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity
Bernhard Nessler, Michael Pfeiffer, Lars Buesing, and Wolfgang Maass · 2013
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Rapid feedforward computation by temporal encoding and learning with spiking neurons
Qiang Yu, Huajin Tang, Kay Chen Tan, and Haizhou Li · 2013
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Neuronal dynamics: From single neurons to networks and models of cognition
Wulfram Gerstner, Werner M Kistler, Richard Naud, and Liam Paninski · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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A brain-inspired spiking neural network model with temporal encoding and learning
Qiang Yu, Huajin Tang, Kay Chen Tan, and Haoyong Yu · 2014
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Spiking deep convolutional neural networks for energy-efficient object recognition
Yongqiang Cao, Yang Chen, and Deepak Khosla · 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.
Spiking deep networks with LIF neurons
Eric Hunsberger and Chris Eliasmith · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 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.
Surrogate gradient learning in spiking neural networks
Emre O Neftci, Hesham Mostafa, and Friedemann Zenke · 2019
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Towards spike-based machine intelligence with neuromorphic computing
Kaushik Roy, Akhilesh Jaiswal, and Priyadarshini Panda · 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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Reinforcement Learning in Spiking Neural Networks with Stochastic and Deterministic Synapses
Mengwen Yuan, Xi Wu, Rui Yan, and Huajin Tang · 2019
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Spike-train level backpropagation for training deep recurrent spiking neural networks
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Neuromodulated spike-timing-dependent plasticity, and theory of three-factor learning rules
Nicolas Frémaux and Wulfram Gerstner · 2016
Cited alongside, same era.
Training deep spiking neural networks using backpropagation
Jun Haeng Lee, Tobi Delbruck, and Michael Pfeiffer · 2016
Cited alongside, same era.
SGDR: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2016
Cited alongside, same era.
Effective sensor fusion with event-based sensors and deep network architectures
Daniel Neil and Shih-Chii Liu · 2016
Cited alongside, same era.
Phased lstm: Accelerating recurrent network training for long or event-based sequences, 2016
Daniel Neil, Michael Pfeiffer, and Shih-Chii Liu · 2016
Cited alongside, same era.
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.
Hierarchical bayesian inference and learning in spiking neural networks
Shangqi Guo, Zhaofei Yu, Fei Deng, Xiaolin Hu, and Feng Chen · 2017
Cited alongside, same era.
Wenrui Zhang and Peng Li · 2019
Later among the works it cites.
Technical report: supervised training of convolutional spiking neural networks with PyTorch
Romain Zimmer, Thomas Pellegrini, Srisht Fateh Singh, and Timothée Masquelier · 2019
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Deep reinforcement learning and its neuroscientific implications
Matthew Botvinick, Jane X. Wang, Will Dabney, Kevin J. Miller, and Zeb Kurth-Nelson · 2020
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LISNN: Improving Spiking Neural Networks with Lateral Interactions for Robust Object Recognition
Xiang Cheng, Yunzhe Hao, Jiaming Xu, and Bo Xu · 2020
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Rethinking the performance comparison between SNNS and ANNS
Lei Deng, Yujie Wu, Xing Hu, Ling Liang, Yufei Ding, Guoqi Li, Guangshe Zhao, Peng Li, and Yuan Xie · 2020
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Haowen Fang, Amar Shrestha, Ziyi Zhao, and Qinru Qiu · 2020
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Spikingjelly
Wei Fang, Yanqi Chen, Jianhao Ding, Ding Chen, Zhaofei Yu, Huihui Zhou, Yonghong Tian, and other contributors · 2020
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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
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Comparing SNNs and RNNs on Neuromorphic Vision Datasets: Similarities and Differences
Weihua He, YuJie Wu, Lei Deng, Guoqi Li, Haoyu Wang, Yang Tian, Wei Ding, Wenhui Wang, and Yuan Xie · 2020
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Synaptic Plasticity Dynamics for Deep Continuous Local Learning (DECOLLE)
Jacques Kaiser, Hesham Mostafa, and Emre Neftci · 2020
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Training Deep Spiking Neural Networks
Eimantas Ledinauskas, Julius Ruseckas, Alfonsas Juršėnas, and Giedrius Buračas · 2020
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Enabling spike-based backpropagation for training deep neural network architectures
Chankyu Lee, Syed Shakib Sarwar, Priyadarshini Panda, Gopalakrishnan Srinivasan, and Kaushik Roy · 2020
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Effective AER Object Classification Using Segmented Probability-Maximization Learning in Spiking Neural Networks
Qianhui Liu, Haibo Ruan, Dong Xing, Huajin Tang, and Gang Pan · 2020
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Nitin Rathi and Kaushik Roy · 2020
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Nitin Rathi, Gopalakrishnan Srinivasan, Priyadarshini Panda, and Kaushik Roy · 2020
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Brain-inspired global-local hybrid learning towards human-like intelligence
Yujie Wu, Rong Zhao, Jun Zhu, Feng Chen, Mingkun Xu, Guoqi Li, Sen Song, Lei Deng, Guanrui Wang, Hao Zheng, et al · 2020
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A new spiking convolutional recurrent neural network (scrnn) with applications to event-based hand gesture recognition
Yannan Xing, Gaetano Di Caterina, and John Soraghan · 2020
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Effective and Efficient Computation with Multiple-timescale Spiking Recurrent Neural Networks
Bojian Yin, Federico Corradi, and Sander M Bohté · 2020
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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 · 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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