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Spiking neural networks (SNNs) are promising in a bio-plausible coding for spatio-temporal information and event-driven signal processing, which is very suited for energy-efficient implementation in neuromorphic hardware.
Comparing SNNs and RNNs on Neuromorphic Vision Datasets: Similarities and Differences
He, W.; Wu, Y.; Deng, L.; Li, G.; Wang, H.; Tian, Y.; Ding, W.; Wang, W.; and Xie, Y. 2020 · 2005
Earlier work this paper cites.
Rathi, N.; Srinivasan, G.; Panda, P.; and Roy, K. 2020 · 2005
Earlier work this paper cites.
ImageNet: A large-scale hierarchical image database
Deng, J.; Dong, W.; Socher, R.; Li, L.; Kai Li; and Li Fei-Fei. 2009 · 2009
Earlier work this paper cites.
A million spiking-neuron integrated circuit with a scalable communication network and interface
Merolla, P. A.; Arthur, J. V.; Alvarez-Icaza, R.; Cassidy, A. S.; Sawada, J.; Akopyan, F.; Jackson, B. L.; Imam, N.; Guo, C.; Nakamura, Y.; Brezzo, B.; Vo, I.; Esser, S. K.; Appuswamy, R.; Taba, B.; Amir, A.; Flickner, M. D.; Risk, W. P.; Manohar, R.; and Modha, D. S. 2014 · 2014
Earlier work this paper cites.
Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing
Diehl, P. U.; Neil, D.; Binas, J.; Cook, M.; Liu, S.; and Pfeiffer, M. 2015 · 2015
Earlier work this paper cites.
Spiking Deep Networks with LIF Neurons
Hunsberger, E.; and Eliasmith, C. 2015 · 2015
Earlier work this paper cites.
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S.; and Szegedy, C. 2015 · 2015
Earlier work this paper cites.
Robustness of spiking Deep Belief Networks to noise and reduced bit precision of neuro-inspired hardware platforms
Stromatias, E.; Neil, D.; Pfeiffer, M.; Galluppi, F.; Furber, S. B.; and Liu, S.-C. 2015 · 2015
Earlier work this paper cites.
Ba, J. L.; Kiros, J. R.; and Hinton, G. E. 2016 · 2016
Earlier work this paper cites.
Deep Residual Learning for Image Recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
Earlier work this paper cites.
Training Deep Spiking Neural Networks Using Backpropagation
Lee, J. H.; Delbruck, T.; and Pfeiffer, M. 2016 · 2016
Cited alongside, same era.
A Low Power, Fully Event-Based Gesture Recognition System
Amir, A.; Taba, B.; Berg, D.; Melano, T.; McKinstry, J.; Di Nolfo, C.; Nayak, T.; Andreopoulos, A.; Garreau, G.; Mendoza, M.; Kusnitz, J.; Debole, M.; Esser, S.; Delbruck, T.; Flickner, M.; and Modha, D. 2017 · 2017
Cited alongside, same era.
CIFAR10-DVS: An Event-Stream Dataset for Object Classification
Li, H.; Liu, H.; Ji, X.; Li, G.; and Shi, L. 2017 · 2017
Cited alongside, same era.
Loihi: A Neuromorphic Manycore Processor with On-Chip Learning
Davies, M.; Srinivasa, N.; Lin, T.; Chinya, G.; Cao, Y.; Choday, S. H.; Dimou, G.; Joshi, P.; Imam, N.; Jain, S.; Liao, Y.; Lin, C.; Lines, A.; Liu, R.; Mathaikutty, D.; McCoy, S.; Paul, A.; Tse, J.; Venkataramanan, G.; Weng, Y.; Wild, A.; Yang, Y.; and Wang, H. 2018 · 2018
Cited alongside, same era.
Hu, Y.; Tang, H.; Wang, Y.; and Pan, G. 2018 · 2018
Cited alongside, same era.
Wu, Y.; and He, K. 2018 · 2018
Later among the works it cites.
DART: Distribution Aware Retinal Transform for Event-based Cameras
Ramesh, B.; Yang, H.; Orchard, G. M.; Le Thi, N. A.; Zhang, S.; and Xiang, C. 2019 · 2019
Later among the works it cites.
Towards spike-based machine intelligence with neuromorphic computing
Roy, K.; Jaiswal, A.; and Panda, P. 2019 · 2019
Later among the works it cites.
Going Deeper in Spiking Neural Networks: VGG and Residual Architectures
Sengupta, A.; Ye, Y.; Wang, R.; Liu, C.; and Roy, K. 2019 · 2019
Later among the works it cites.
Direct training for spiking neural networks: Faster, larger, better
Wu, Y.; Deng, L.; Li, G.; Zhu, J.; Xie, Y.; and Shi, L. 2019 · 2019
Later among the works it cites.
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Hybrid Macro/Micro Level Backpropagation for Training Deep Spiking Neural Networks
Jin, Y.; Zhang, W.; and Li, P. 2018 · 2018
Cited alongside, same era.
SLAYER: Spike Layer Error Reassignment in Time
Shrestha, S. B.; and Orchard, G. 2018 · 2018
Cited alongside, same era.
HATS: Histograms of Averaged Time Surfaces for Robust Event-Based Object Classification
Sironi, A.; Brambilla, M.; Bourdis, N.; Lagorce, X.; and Benosman, R. 2018 · 2018
Cited alongside, same era.
Spatio-Temporal Backpropagation for Training High-Performance Spiking Neural Networks
Wu, Y.; Deng, L.; Li, G.; Zhu, J.; and Shi, L. 2018 · 2018
Cited alongside, same era.
Chen, Z.; Deng, L.; Wang, B.; Li, G.; and Xie, Y. 2020 · 2020
Closest in time.
RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper High-Accuracy and Low-Latency Spiking Neural Network
Han, B.; Srinivasan, G.; and Roy, K. 2020 · 2020
Closest in time.
Efficient Processing of Spatio-Temporal Data Streams With Spiking Neural Networks
Kugele, A.; Pfeil, T.; Pfeiffer, M.; and Chicca, E. 2020 · 2020
Closest in time.
Enabling Spike-Based Backpropagation for Training Deep Neural Network Architectures
Lee, C.; Sarwar, S. S.; Panda, P.; Srinivasan, G.; and Roy, K. 2020 · 2020
Closest in time.