@inproceedings{d38a3a8e95234cc58f1619e2a0282de2,
title = "Improving the stability for spiking neural networks using anti-noise learning rule",
abstract = "Most of the existing SNNs only consider training the noise-free data. However, noise extensively exists in actual SNNs. The stability of networks is affected by noise perturbation during the training period. Therefore, one research challenge is to improve the stability and produce reliable outputs under the present of noises. In this paper, the training method and the exponential method are employed to enhance the neural network ability of noise tolerance. The comparison of conventional and anti-noise SNNs under various tasks shows that the anti-noise SNN can significantly improve the noise tolerance capability.",
keywords = "Learning rule, Noise tolerance, Spiking neural network, Stability",
author = "Yuling Luo and Qiang Fu and Junxiu Liu and Yongchuang Huang and Xuemei Ding and Yi Cao",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2018.; 15th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2018 ; Conference date: 28-08-2018 Through 31-08-2018",
year = "2018",
doi = "10.1007/978-3-319-97310-4_4",
language = "English",
isbn = "9783319973098",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "29--37",
editor = "Xin Geng and Byeong-Ho Kang",
booktitle = "PRICAI 2018",
}