GLSNN: A Multi-Layer Spiking Neural Network Based on Global Feedback Alignment and Local STDP Plasticity

doi:10.3389/fncom.2020.576841

	GLSNN: A Multi-Layer Spiking Neural Network Based on Global Feedback Alignment and Local STDP Plasticity
	Zhao, Dongcheng1,2 ; Zeng, Yi1,2,3,4 ; Zhang, Tielin1 ; Shi, Mengting1,2 ; Zhao, Feifei1
发表期刊	FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
	2020-11-12
卷号	14 页码:12
通讯作者	Zeng, Yi(yi.zeng@ia.ac.cn)
摘要	Spiking Neural Networks (SNNs) are considered as the third generation of artificial neural networks, which are more closely with information processing in biological brains. However, it is still a challenge for how to train the non-differential SNN efficiently and robustly with the form of spikes. Here we give an alternative method to train SNNs by biologically-plausible structural and functional inspirations from the brain. Firstly, inspired by the significant top-down structural connections, a global random feedback alignment is designed to help the SNN propagate the error target from the output layer directly to the previous few layers. Then inspired by the local plasticity of the biological system in which the synapses are more tuned by the neighborhood neurons, a differential STDP is used to optimize local plasticity. Extensive experimental results on the benchmark MNIST (98.62%) and Fashion MNIST (89.05%) have shown that the proposed algorithm performs favorably against several state-of-the-art SNNs trained with backpropagation.
关键词	SNN plasticity brain local STDP global feedback alignment
DOI	10.3389/fncom.2020.576841
关键词[WOS]	DYNAMICAL SYNAPSES ; NEURONS ; MODELS
收录类别	SCI
语种	英语
资助项目	Strategic Priority Research Program of the Chinese Academy of Sciences[XDB32070100] ; Ministry of Science and Technology of the People's Republic of China[2020AAA0104305] ; Beijing Municipal Commission of Science and Technology[Z181100001518006] ; CETC Joint Fund[6141B08010103] ; Beijing Academy of Artificial Intelligence (BAAI)
项目资助者	Strategic Priority Research Program of the Chinese Academy of Sciences ; Ministry of Science and Technology of the People's Republic of China ; Beijing Municipal Commission of Science and Technology ; CETC Joint Fund ; Beijing Academy of Artificial Intelligence (BAAI)
WOS研究方向	Mathematical & Computational Biology ; Neurosciences & Neurology
WOS类目	Mathematical & Computational Biology ; Neurosciences
WOS记录号	WOS:000592195800001
出版者	FRONTIERS MEDIA SA
七大方向——子方向分类	类脑模型与计算
引用统计	被引频次：29[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/41775
专题	脑图谱与类脑智能实验室_类脑认知计算
通讯作者	Zeng, Yi
作者单位	1.Chinese Acad Sci, Inst Automat, Res Ctr Brain Inspired Intelligence, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 3.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing, Peoples R China 4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
第一作者单位	类脑智能研究中心
通讯作者单位	类脑智能研究中心; 模式识别国家重点实验室
推荐引用方式 GB/T 7714	Zhao, Dongcheng,Zeng, Yi,Zhang, Tielin,et al. GLSNN: A Multi-Layer Spiking Neural Network Based on Global Feedback Alignment and Local STDP Plasticity[J]. FRONTIERS IN COMPUTATIONAL NEUROSCIENCE,2020,14:12.
APA	Zhao, Dongcheng,Zeng, Yi,Zhang, Tielin,Shi, Mengting,&Zhao, Feifei.(2020).GLSNN: A Multi-Layer Spiking Neural Network Based on Global Feedback Alignment and Local STDP Plasticity.FRONTIERS IN COMPUTATIONAL NEUROSCIENCE,14,12.
MLA	Zhao, Dongcheng,et al."GLSNN: A Multi-Layer Spiking Neural Network Based on Global Feedback Alignment and Local STDP Plasticity".FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 14(2020):12.

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