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Spiking neural networks (SNNs) are known as a typical kind of brain-inspired models with their unique features of rich neuronal dynamics, diverse coding schemes and low power consumption properties.
P. J. Werbos, “Backpropagation through time: what it does and how to do it,” Proceedings of the IEEE , vol. 78, no. 10, pp. 1550–1560, 1990
1990
Earlier work this paper cites.
M. W., “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.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in CVPR . Ieee, 2009, pp. 248–255
2009
Earlier work this paper cites.
2012
Earlier work this paper cites.
P. O’Connor, D. Neil, S.-C. Liu, T. Delbruck, and M. Pfeiffer, “Real-time classification and sensor fusion with a spiking deep belief network,” Frontiers in neuroscience , vol. 7, p. 178, 2013
2013
Earlier work this paper cites.
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?” in Proceedings of the 27th International Conference on Neural Information Processing Systems , vol. 2, 2014, pp. 3320–3328
2014
Earlier work this paper cites.
C. Brandli, R. Berner, M. Yang, S. Liu, and T. Delbruck, “A 240 ×180 130 db 3 µs latency global shutter spatiotemporal vision sensor,” IEEE Journal of Solid-State Circuits , vol. 49, no. 10, pp. 2333–2341, 2014
2014
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. Donahue, L. Anne Hendricks, S. Guadarrama, M. Rohrbach, S. Venugopalan, K. Saenko, and T. Darrell, “Long-term recurrent convolutional networks for visual recognition and description,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 2625–2634
2015
Earlier work this paper cites.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “Imagenet large scale visual recognition challenge,” IJCV , vol. 115, no. 3, pp. 211–252, 2015
2015
Earlier work this paper cites.
Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky, “Domain-adversarial training of neural networks,” The journal of machine learning research , vol. 17, no. 1, pp. 2096–2030, 2016
2016
Earlier work this paper cites.
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. Li, H. Liu, X. Ji, G. Li, and L. Shi, “Cifar10-dvs: an event-stream dataset for object classification,” Frontiers in neuroscience , vol. 11, p. 309, 2017
2017
Cited alongside, same era.
A. Roy, K.and Jaiswal and P. Panda, “Towards spike-based machine intelligence with neuromorphic computing,” Nature , vol. 575, no. 7784, pp. 607–617, 2019
2019
Cited alongside, same era.
J. Pei, L. Deng, S. Song, M. Zhao, Y. Zhang, S. Wu, and et al., “Towards artificial general intelligence with hybrid tianjic chip architecture,” Nature , vol. 572, no. 7767, pp. 106–111, 2019
2019
Cited alongside, same era.
S. Lu and A. Sengupta, “Exploring the connection between binary and spiking neural networks,” Frontiers in Neuroscience , vol. 14, 2020
2020
Later among the works it cites.
Y. Bi, A. Chadha, A. Abbas, E. Bourtsoulatze, and Y. Andreopoulos, “Graph-based spatio-temporal feature learning for neuromorphic vision sensing,” IEEE Transactions on Image Processing , vol. 29, pp. 9084–9098, 2020
2020
Later among the works it cites.
Z. Wu, H. Zhang, Y. Lin, G. Li, M. Wang, and Y. Tang, “Liaf-net: Leaky integrate and analog fire network for lightweight and efficient spatiotemporal information processing,” IEEE Transactions on Neural Networks and Learning Systems , 2021
2021
Later among the works it cites.
H. Zheng, Y. Wu, L. Deng, Y. Hu, and G. Li, “Going deeper with directly-trained larger spiking neural networks,” Proceedings of the AAAI Conference on Artificial Intelligence , vol. 35, no. 12, pp. 11 062–11 070, May 2021
2021
Later among the works it cites.
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2019
Cited alongside, same era.
W. Severa, C. M. Vineyard, R. Dellana, S. J. Verzi, and J. B. Aimone, “Training deep neural networks for binary communication with the whetstone method,” Nature Machine Intelligence , vol. 1, no. 2, pp. 86–94, 2019
2019
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, 2019
2019
Cited alongside, same era.
Y. Bi, A. Chadha, A. Abbas, E. Bourtsoulatze, and Y. Andreopoulos, “Graph-based object classification for neuromorphic vision sensing,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 491–501
2019
Cited alongside, same era.
S. Kim, S. Park, B. Na, and S. Yoon, “Spiking-yolo: Spiking neural network for energy-efficient object detection,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 07, 2020, pp. 11 270–11 277
2020
Cited alongside, same era.
N. Rathi, G. Srinivasan, P. Panda, and K. Roy, “Enabling deep spiking neural networks with hybrid conversion and spike timing dependent backpropagation,” in International Conference on Learning Representations , 2020
2020
Cited alongside, same era.
2021
Later among the works it cites.
J. Wu, Y. Chua, M. Zhang, G. Li, H. Li, and K. C. Tan, “A tandem learning rule for effective training and rapid inference of deep spiking neural networks,” IEEE Transactions on Neural Networks and Learning Systems , pp. 1–15, 2021
2021
Later among the works it cites.
“Hybrid neural state machine for neural network,” Science China Information Sciences , vol. 64, no. 3, p. 132202, 2021. [Online]. Available: https://doi.org/10.1007/s11432-019-2988-1
2021
Later among the works it cites.
Y. Lin, W. Ding, S. Qiang, L. Deng, and G. Li, “Es-imagenet: A million event-stream classification dataset for spiking neural networks,” Frontiers in neuroscience , 2021
2021
Later among the works it cites.
N. Rathi and K. Roy, “{DIET}-{snn}: A low-latency spiking neural network with direct input encoding & leakage and threshold optimization,” 2021. [Online]. Available: https://openreview.net/forum?id=u_bGm5lrm72
2021
Later among the works it cites.
W. Fang, Z. Yu, Y. Chen, T. Masquelier, T. Huang, and Y. Tian, “Incorporating learnable membrane time constant to enhance learning of spiking neural networks,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 2661–2671
2021
Later among the works it cites.