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Spiking Neural Networks (SNNs), as bio-inspired energy-efficient neural networks, have attracted great attentions from researchers and industry.
Multiple slope analog-to-digital converter, June 14 1994
Benjamin Eng Jr and Don P Matson · 1994
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Rate coding versus temporal order coding: what the retinal ganglion cells tell the visual cortex
Rufin Van Rullen and Simon J Thorpe · 2001
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SpiNNaker: A 1-w 18-core system-on-chip for massively-parallel neural network simulation
Eustace Painkras, Luis A Plana, Jim Garside, Steve Temple, Francesco Galluppi, Cameron Patterson, David R Lester, Andrew D Brown, and Steve B Furber · 2013
Earlier work this paper cites.
Spiking deep convolutional neural networks for energy-efficient object recognition
Yongqiang Cao, Yang Chen, and Deepak Khosla · 2015
Earlier work this paper cites.
Unsupervised learning of digit recognition using spike-timing-dependent plasticity
Peter U Diehl and Matthew Cook · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Spiking deep networks with LIF neurons
Eric Hunsberger and Chris Eliasmith · 2015
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Scalable energy-efficient, low-latency implementations of trained spiking deep belief networks on SpiNNaker
Evangelos Stromatias, Daniel Neil, Francesco Galluppi, Michael Pfeiffer, Shih-Chii Liu, and Steve Furber · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Learning to be efficient: Algorithms for training low-latency, low-compute deep spiking neural networks
Daniel Neil, Michael Pfeiffer, and Shih-Chii Liu · 2016
Cited alongside, same era.
Theory and tools for the conversion of analog to spiking convolutional neural networks
Bodo Rueckauer, Iulia-Alexandra Lungu, Yuhuang Hu, and Michael Pfeiffer · 2016
Cited alongside, same era.
Conversion of continuous-valued deep networks to efficient event-driven networks for image classification
Bodo Rueckauer, Iulia-Alexandra Lungu, Yuhuang Hu, Michael Pfeiffer, and Shih-Chii Liu · 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.
Yangfan Hu, Huajin Tang, Yueming Wang, and Gang Pan · 2018
BP-STDP: Approximating backpropagation using spike timing dependent plasticity
Amirhossein Tavanaei and Anthony Maida · 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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Language models are few-shot learners
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Deep spiking neural network: Energy efficiency through time based coding
Bing Han and Kaushik Roy · 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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Cited alongside, same era.
STDP-based spiking deep convolutional neural networks for object recognition
Saeed Reza Kheradpisheh, Mohammad Ganjtabesh, Simon J Thorpe, and Timothée Masquelier · 2018
Cited alongside, same era.
Training deep spiking convolutional neural networks with STDP-based unsupervised pre-training followed by supervised fine-tuning
Chankyu Lee, Priyadarshini Panda, Gopalakrishnan Srinivasan, and Kaushik Roy · 2018
Cited alongside, same era.
Dota 2 with large scale deep reinforcement learning
Christopher Berner, Greg Brockman, Brooke Chan, Vicki Cheung, Przemysław Dębiak, Christy Dennison, David Farhi, Quirin Fischer, Shariq Hashme, Chris Hesse, et al · 2019
Cited alongside, same era.
TrueNorth: Accelerating from zero to 64 million neurons in 10 years
Michael V DeBole, Brian Taba, Arnon Amir, Filipp Akopyan, Alexander Andreopoulos, William P Risk, Jeff Kusnitz, Carlos Ortega Otero, Tapan K Nayak, Rathinakumar Appuswamy, et al · 2019
Cited alongside, same era.
Going deeper in spiking neural networks: VGG and residual architectures
Abhronil Sengupta, Yuting Ye, Robert Wang, Chiao Liu, and Kaushik Roy · 2019
Cited alongside, same era.
Spiking-YOLO: Spiking neural network for energy-efficient object detection
Seijoon Kim, Seongsik Park, Byunggook Na, and Sungroh Yoon · 2020
Later among the works it cites.
Enabling deep spiking neural networks with hybrid conversion and spike timing dependent backpropagation
Nitin Rathi, Gopalakrishnan Srinivasan, Priyadarshini Panda, and Kaushik Roy · 2020
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Optimized spiking neurons can classify images with high accuracy through temporal coding with two spikes
Christoph Stöckl and Wolfgang Maass · 2021
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Friedemann Zenke and Tim P Vogels · 2021
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