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.
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Acknowledgement
This research was supported by the National Natural Science Foundation of China under Grant 61603104, the Guangxi Natural Science Foundation under Grants 2016GXNSFCA380017, 2015GXNSFBA139256 and 2017GXNSFAA198180, the funding of Overseas 100 Talents Program of Guangxi Higher Education, and the Doctoral Research Foundation of Guangxi Normal University under Grant 2016BQ005.
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Luo, Y., Fu, Q., Liu, J., Huang, Y., Ding, X., Cao, Y. (2018). Improving the Stability for Spiking Neural Networks Using Anti-noise Learning Rule. In: Geng, X., Kang, BH. (eds) PRICAI 2018: Trends in Artificial Intelligence. PRICAI 2018. Lecture Notes in Computer Science(), vol 11013. Springer, Cham. https://doi.org/10.1007/978-3-319-97310-4_4
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DOI: https://doi.org/10.1007/978-3-319-97310-4_4
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