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Spiking CapsNet: A spiking neural network with a biologically plausible routing rule between capsules

文献类型：期刊论文

作者Zhao, Dongcheng4,5; Li, Yang1,4,5; Zeng, Yi1,2,3,4,5; Wang, Jihang1,4,5; Zhang, Qian1,4,5
刊名INFORMATION SCIENCES
出版日期2022-09-01
卷号610页码:1-13
关键词Spiking Neural Network Capsual Neural Netowrk Biologically Plausible Routing Noise Robustness Affine Transformation Robustness
ISSN号0020-0255
DOI10.1016/j.ins.2022.07.152
通讯作者Zeng, Yi(yi.zeng@ia.ac.cn)
英文摘要Spiking neural network (SNN) has attracted much attention due to its powerful spatiotemporal information representation ability. Capsule Neural Network (CapsNet) does well in assembling and coupling features of different network layers. Here, we propose Spiking CapsNet by combining spiking neurons and capsule structures. In addition, we propose a more biologically plausible Spike Timing Dependent Plasticity routing mechanism. The coupling ability is further improved by fully considering the spatio-temporal relationship between spiking capsules of the low layer and the high layer. We have verified experiments on the MNIST, FashionMNIST, and CIFAR10 datasets. Our algorithm still shows comparable performance concerning other excellent SNNs with typical structures (convolutional, fully-connected) on these classification tasks. Our Spiking CapsNet combines SNN and CapsNet's strengths and shows strong robustness to noise and affine transformation. By adding different Salt-Pepper and Gaussian noise to the test dataset, the experimental results demonstrate that our algorithm is more resistant to noise than other approaches. As well, our Spiking CapsNet shows strong generalization to affine transformation on the AffNIST dataset. Our code is available at https://github.com/BrainCog-X/Brain-Cog. (C) 2022 The Author(s). Published by Elsevier Inc.
资助项目National Key Research and Development Program[2020AAA0104305] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB32070100]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000848341500001
出版者ELSEVIER SCIENCE INC
资助机构National Key Research and Development Program ; Strategic Priority Research Program of the Chinese Academy of Sciences
源URL[http://ir.ia.ac.cn/handle/173211/50041]  
专题类脑智能研究中心_类脑认知计算
通讯作者Zeng, Yi
作者单位1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
2.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai, Peoples R China
3.CASIA, Natl Lab Pattern Recognit, Beijing, Peoples R China
4.CASIA, Res Ctr Brain Inspired Intelligence, Beijing, Peoples R China
5.Chinese Acad Sci CASIA, Inst Automat, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Zhao, Dongcheng,Li, Yang,Zeng, Yi,et al. Spiking CapsNet: A spiking neural network with a biologically plausible routing rule between capsules[J]. INFORMATION SCIENCES,2022,610:1-13.
APA Zhao, Dongcheng,Li, Yang,Zeng, Yi,Wang, Jihang,&Zhang, Qian.(2022).Spiking CapsNet: A spiking neural network with a biologically plausible routing rule between capsules.INFORMATION SCIENCES,610,1-13.
MLA Zhao, Dongcheng,et al."Spiking CapsNet: A spiking neural network with a biologically plausible routing rule between capsules".INFORMATION SCIENCES 610(2022):1-13.

入库方式： OAI收割

来源：自动化研究所

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