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Deep spiking neural networks (SNNs) support asynchronous event-driven computation, massive parallelism and demonstrate great potential to improve the energy efficiency of its synchronous analog counterpart.
“Networks of spiking neurons: the third generation of neural network models,”
W. Maass, · 1997
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Theoretical neuroscience
P. Dayan and L. F. Abbott, · 2001
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Spiking neuron models: Single neurons, populations, plasticity
W. Gerstner and W. M. Kistler, · 2002
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“Multiple neural spike train data analysis: state-of-the-art and future challenges,”
E. N. Brown, R. E. Kass, and P. P. Mitra, · 2004
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“Uci machine learning repository,” 2007
A. Asuncion and D. Newman, · 2007
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“Imagenet: A large-scale hierarchical image database,”
J. Deng, W. Dong, R. Socher, L. Li, Kai Li, and Li Fei-Fei, · 2009
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“Imagenet classification with deep convolutional neural networks,”
A. Krizhevsky, I. Sutskever, and G. E. Hinton, · 2012
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“Adam: A method for stochastic optimization,”
D. P. Kingma and J. Ba, · 2014
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“Very deep convolutional networks for large-scale image recognition,”
K. Simonyan and A. Zisserman, · 2014
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“Deep learning,”
Y. LeCun, Y. Bengio, and G. Hinton, · 2015
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“Spiking deep convolutional neural networks for energy-efficient object recognition,”
Y. Cao, Y. Chen, and D. Khosla, · 2015
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“Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing,”
P. U. Diehl, D. Neil, J. Binas, M. Cook, S. C. Liu, and M. Pfeiffer, · 2015
Cited alongside, same era.
“Training deep spiking neural networks using backpropagation,”
J. H. Lee, T. Delbruck, and M. Pfeiffer, · 2016
Cited alongside, same era.
P. O’Connor and M. Welling, · 2016
Cited alongside, same era.
“Learning to be efficient: algorithms for training low-latency, low-compute deep spiking neural networks,”
D. Neil, M. Pfeiffer, and S. C. Liu, · 2016
Cited alongside, same era.
“You only look once: Unified, real-time object detection,”
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, · 2016
Cited alongside, same era.
“A spiking neural network framework for robust sound classification,”
J. Wu, Y. Chua, M. Zhang, H. Li, and K. C. Tan, · 2018
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“Deep learning in spiking neural networks,”
A. Tavanaei, M. Ghodrati, S. R. Kheradpisheh, T. Masquelier, and A. S. Maida, · 2018
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“Slayer: Spike layer error reassignment in time,”
S. B. Shrestha and G. Orchard, · 2018
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“Direct training for spiking neural networks: Faster, larger, better,”
Y. Wu, L. Deng, G. Li, J. Zhu, and L. Shi, · 2018
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“Supervised learning based on temporal coding in spiking neural networks,”
H. Mostafa, · 2018
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“An event-driven classifier for spiking neural networks fed with synthetic or dynamic vision sensor data,”
E. Stromatias, M. Soto, T. Serrano-Gotarredona, and B. Linares-Barranco, · 2017
Cited alongside, same era.
“Conversion of continuous-valued deep networks to efficient event-driven networks for image classification,”
B. Rueckauer, I. A. Lungu, Y. Hu, M. Pfeiffer, and S. C. Liu, · 2017
Cited alongside, same era.
“Spiketemp: An enhanced rank-order-based learning approach for spiking neural networks with adaptive structure,”
J. Wang, A. Belatreche, L. P. Maguire, and T. M. McGinnity, · 2017
Cited alongside, same era.
“Event-driven random back-propagation: Enabling neuromorphic deep learning machines,”
E. O. Neftci, C. Augustine, S. Paul, and G. Detorakis, · 2017
Cited alongside, same era.
“Deep learning with spiking neurons: Opportunities & challenges,”
M. Pfeiffer and T. Pfeil, · 2018
Cited alongside, same era.
L. R. Iyer, Y. Chua, and H. Li, · 2018
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“Going deeper in spiking neural networks: Vgg and residual architectures,”
A. Sengupta, Y.g Ye, C. Wang, R.and Liu, and K. Roy, · 2018
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“Spiking deep residual network,”
Y. Hu, H. Tang, Y. Wang, and G. Pan, · 2018
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“Spatio-temporal backpropagation for training high-performance spiking neural networks,”
Y. Wu, L. Deng, G. Li, J. Zhu, and L. Shi, · 2018
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“Stdp-based spiking deep convolutional neural networks for object recognition,”
S. R. Kheradpisheh, M. Ganjtabesh, S. J. Thorpe, and T. Masquelier, · 2018
Later among the works it cites.