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The spiking neural network (SNN) computes and communicates information through discrete binary events.
C. Blakemore, R. H. Carpenter, and M. A. Georgeson, “Lateral inhibition between orientation detectors in the human visual system,” Nature , vol. 228, no. 5266, pp. 37–39, 1970
1970
Earlier work this paper cites.
M. Ito, “Long-term depression,” Annual review of neuroscience , vol. 12, no. 1, pp. 85–102, 1989
1989
Earlier work this paper cites.
E. Marder, L. Abbott, G. G. Turrigiano, Z. Liu, and J. Golowasch, “Memory from the dynamics of intrinsic membrane currents,” Proceedings of the national academy of sciences , vol. 93, no. 24, pp. 13 481–13 486, 1996
1996
Earlier work this paper cites.
W. Maass, “Networks of spiking neurons: the third generation of neural network models,” Neural networks , vol. 10, no. 9, pp. 1659–1671, 1997
1997
Earlier work this paper cites.
G.-q. Bi and M.-m. Poo, “Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type,” Journal of neuroscience , vol. 18, no. 24, pp. 10 464–10 472, 1998
1998
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , vol. 86, no. 11, pp. 2278–2324, 1998
1998
Earlier work this paper cites.
L. F. Abbott and S. B. Nelson, “Synaptic plasticity: taming the beast,” Nature neuroscience , vol. 3, no. 11, pp. 1178–1183, 2000
2000
Earlier work this paper cites.
I. Marian, R. Reilly, and D. Mackey, “Efficient event-driven simulation of spiking neural networks,” 2002
2002
Earlier work this paper cites.
R. C. Malenka, “The long-term potential of ltp,” Nature Reviews Neuroscience , vol. 4, no. 11, pp. 923–926, 2003
2003
Earlier work this paper cites.
E. M. Izhikevich, “Simple model of spiking neurons,” IEEE Transactions on neural networks , vol. 14, no. 6, pp. 1569–1572, 2003
2003
Earlier work this paper cites.
W. S. Noble, “What is a support vector machine?” Nature biotechnology , vol. 24, no. 12, pp. 1565–1567, 2006
2006
Earlier work this paper cites.
A. Krizhevsky, G. Hinton et al. , “Learning multiple layers of features from tiny images,” 2009
2009
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE conference on computer vision and pattern recognition . Ieee, 2009, pp. 248–255
2009
Earlier work this paper cites.
2014
Earlier work this paper cites.
T. Chen, Z. Du, N. Sun, J. Wang, C. Wu, Y. Chen, and O. Temam, “Diannao: A small-footprint high-throughput accelerator for ubiquitous machine-learning,” ACM SIGARCH Computer Architecture News , vol. 42, no. 1, pp. 269–284, 2014
2014
Earlier work this paper cites.
B. Zhao, Q. Yu, R. Ding, S. Chen, and H. Tang, “Event-driven simulation of the tempotron spiking neuron,” in 2014 IEEE Biomedical Circuits and Systems Conference (BioCAS) Proceedings . IEEE, 2014, pp. 667–670
2014
Earlier work this paper cites.
R. Girshick, “Fast r-cnn,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 1440–1448
2015
Earlier work this paper cites.
A. Nguyen, J. Yosinski, and J. Clune, “Deep neural networks are easily fooled: High confidence predictions for unrecognizable images,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 427–436
2015
Earlier work this paper cites.
B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum, “Human-level concept learning through probabilistic program induction,” Science , vol. 350, no. 6266, pp. 1332–1338, 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
P. U. Diehl and M. Cook, “Unsupervised learning of digit recognition using spike-timing-dependent plasticity,” Frontiers in computational neuroscience , vol. 9, p. 99, 2015
2015
Earlier work this paper cites.
Y. Cao, Y. Chen, and D. Khosla, “Spiking deep convolutional neural networks for energy-efficient object recognition,” International Journal of Computer Vision , vol. 113, no. 1, pp. 54–66, 2015
2015
Earlier work this paper cites.
P. U. Diehl, D. Neil, J. Binas, M. Cook, S.-C. Liu, and M. Pfeiffer, “Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing,” in 2015 International Joint Conference on Neural Networks (IJCNN) . ieee, 2015, pp. 1–8
2015
Earlier work this paper cites.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 779–788
2016
Cited alongside, same era.
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” in European conference on computer vision . Springer, 2016, pp. 21–37
2016
Cited alongside, same era.
J. Wu, C. Leng, Y. Wang, Q. Hu, and J. Cheng, “Quantized convolutional neural networks for mobile devices,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 4820–4828
2016
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Cited alongside, same era.
H. Jang, O. Simeone, B. Gardner, and A. Gruning, “An introduction to probabilistic spiking neural networks: Probabilistic models, learning rules, and applications,” IEEE Signal Processing Magazine , vol. 36, no. 6, pp. 64–77, 2019
2019
Later among the works it cites.
K. Roy, A. Jaiswal, and P. Panda, “Towards spike-based machine intelligence with neuromorphic computing,” Nature , vol. 575, no. 7784, pp. 607–617, 2019
2019
Later among the works it cites.
M. Davies, “Benchmarks for progress in neuromorphic computing,” Nature Machine Intelligence , vol. 1, no. 9, pp. 386–388, 2019
2019
Later among the works it cites.
Y. Wu, L. Deng, G. Li, J. Zhu, Y. Xie, and L. Shi, “Direct training for spiking neural networks: Faster, larger, better,” in Proceedings of the AAAI Conference on Artificial Intelligence , 2019, pp. 1311–1318
2019
Later among the works it cites.
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2017
Cited alongside, same era.
G. W. Burr, R. M. Shelby, A. Sebastian, S. Kim, S. Kim, S. Sidler, K. Virwani, M. Ishii, P. Narayanan, A. Fumarola et al. , “Neuromorphic computing using non-volatile memory,” Advances in Physics: X , vol. 2, no. 1, pp. 89–124, 2017
2017
Cited alongside, same era.
Y. Zeng, T. Zhang, and B. Xu, “Improving multi-layer spiking neural networks by incorporating brain-inspired rules,” Science China Information Sciences , vol. 60, no. 5, p. 052201, 2017
2017
Cited alongside, same era.
B. Rueckauer, I.-A. Lungu, Y. Hu, M. Pfeiffer, and S.-C. Liu, “Conversion of continuous-valued deep networks to efficient event-driven networks for image classification,” Frontiers in neuroscience , vol. 11, p. 682, 2017
2017
Cited alongside, same era.
2018
Cited alongside, same era.
R. Kemker, M. McClure, A. Abitino, T. Hayes, and C. Kanan, “Measuring catastrophic forgetting in neural networks,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 32, no. 1, 2018
2018
Cited alongside, same era.
X. He and J. Cheng, “Learning compression from limited unlabeled data,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 752–769
2018
Cited alongside, same era.
Z. Bing, C. Meschede, F. Röhrbein, K. Huang, and A. C. Knoll, “A survey of robotics control based on learning-inspired spiking neural networks,” Frontiers in neurorobotics , vol. 12, p. 35, 2018
2018
Cited alongside, same era.
A. Sengupta, Y. Ye, R. Wang, C. Liu, and K. Roy, “Going deeper in spiking neural networks: Vgg and residual architectures,” Frontiers in neuroscience , vol. 13, p. 95, 2019
2019
Later among the works it cites.
F. Xing, Y. Yuan, H. Huo, and T. Fang, “Homeostasis-based cnn-to-snn conversion of inception and residual architectures,” in International Conference on Neural Information Processing . Springer, 2019, pp. 173–184
2019
Later among the works it cites.
L. Zhang, S. Zhou, T. Zhi, Z. Du, and Y. Chen, “Tdsnn: From deep neural networks to deep spike neural networks with temporal-coding,” in Proceedings of the AAAI Conference on Artificial Intelligence , 2019, pp. 1319–1326
2019
Later among the works it cites.
S. Park, S. Kim, H. Choe, and S. Yoon, “Fast and efficient information transmission with burst spikes in deep spiking neural networks,” in 2019 56th ACM/IEEE Design Automation Conference (DAC) . IEEE, 2019, pp. 1–6
2019
Later among the works it cites.
Z. Yan, J. Zhou, and W.-F. Wong, “Near lossless transfer learning for spiking neural networks,” 2021
2019
Later among the works it cites.
2020
Later among the works it cites.
X. Wang, X. Lin, and X. Dang, “Supervised learning in spiking neural networks: A review of algorithms and evaluations,” Neural Networks , vol. 125, pp. 258–280, 2020
2020
Later among the works it cites.
J. L. Lobo, J. Del Ser, A. Bifet, and N. Kasabov, “Spiking neural networks and online learning: An overview and perspectives,” Neural Networks , vol. 121, pp. 88–100, 2020
2020
Later among the works it cites.
S. Song, A. Balaji, A. Das, N. Kandasamy, and J. Shackleford, “Compiling spiking neural networks to neuromorphic hardware,” in The 21st ACM SIGPLAN/SIGBED Conference on Languages, Compilers, and Tools for Embedded Systems , 2020, pp. 38–50
2020
Later among the works it cites.
Y. Hao, X. Huang, M. Dong, and B. Xu, “A biologically plausible supervised learning method for spiking neural networks using the symmetric stdp rule,” Neural Networks , vol. 121, pp. 387–395, 2020
2020
Later among the works it cites.
C. Lee, S. S. Sarwar, P. Panda, G. Srinivasan, and K. Roy, “Enabling spike-based backpropagation for training deep neural network architectures,” Frontiers in neuroscience , vol. 14, 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
X. Yang, Z. Zhang, W. Zhu, S. Yu, L. Liu, and N. Wu, “Deterministic conversion rule for cnns to efficient spiking convolutional neural networks,” Science China Information Sciences , vol. 63, no. 2, p. 122402, 2020
2020
Later among the works it cites.
B. Han, G. Srinivasan, and K. Roy, “Rmp-snn: Residual membrane potential neuron for enabling deeper high-accuracy and low-latency spiking neural network,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 13 558–13 567
2020
Later among the works it cites.
2020
Later among the works it cites.
B. Han and K. Roy, “Deep spiking neural network: Energy efficiency through time based coding,” in Proc. IEEE Eur. Conf. Comput. Vis.(ECCV) , 2020, pp. 388–404
2020
Later among the works it cites.
Q. Liang and Y. Zeng, “Stylistic composition of melodies based on a brain-inspired spiking neural network,” Frontiers in systems neuroscience , vol. 15, p. 21, 2021
2021
Closest in time.
H. Fang, Y. Zeng, and F. Zhao, “Brain inspired sequences production by spiking neural networks with reward-modulated stdp,” Frontiers in Computational Neuroscience , vol. 15, p. 8, 2021
2021
Closest in time.