Neuronal-Plasticity and Reward-Propagation Improved Recurrent Spiking Neural Networks

doi:10.3389/fnins.2021.654786

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	Neuronal-Plasticity and Reward-Propagation Improved Recurrent Spiking Neural Networks
	Jia, Shuncheng 1,4; Zhang, Tielin 1,4; Cheng, Xiang 1,4; Liu, Hongxing 3,4; Xu, Bo 1,2,4
刊名	FRONTIERS IN NEUROSCIENCE
	2021-03-12
卷号	15 页码:11
关键词	spiking neural network neuronal plasticity synaptic plasticity reward propagation sparse connections
DOI	10.3389/fnins.2021.654786
通讯作者	Zhang, Tielin(tielin.zhang@ia.ac.cn) ; Xu, Bo(xubo@ia.ac.cn)
英文摘要	Different types of dynamics and plasticity principles found through natural neural networks have been well-applied on Spiking neural networks (SNNs) because of their biologically-plausible efficient and robust computations compared to their counterpart deep neural networks (DNNs). Here, we further propose a special Neuronal-plasticity and Reward-propagation improved Recurrent SNN (NRR-SNN). The historically-related adaptive threshold with two channels is highlighted as important neuronal plasticity for increasing the neuronal dynamics, and then global labels instead of errors are used as a reward for the paralleling gradient propagation. Besides, a recurrent loop with proper sparseness is designed for robust computation. Higher accuracy and stronger robust computation are achieved on two sequential datasets (i.e., TIDigits and TIMIT datasets), which to some extent, shows the power of the proposed NRR-SNN with biologically-plausible improvements.
资助项目	National Key R&D Program of China[2020AAA0104305] ; National Natural Science Foundation of China[61806195] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB32070100] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA27010404] ; Beijing Brain Science Project[Z181100001518006]
WOS研究方向	Neurosciences & Neurology
语种	英语
出版者	FRONTIERS MEDIA SA
WOS记录号	WOS:000632907700001
资助机构	National Key R&D Program of China ; National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Beijing Brain Science Project
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/44003]
专题	数字内容技术与服务研究中心_听觉模型与认知计算
通讯作者	Zhang, Tielin; Xu, Bo
作者单位	1.Univ Chinese Acad Sci UCAS, Sch Artificial Intelligence, Beijing, Peoples R China 2.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai, Peoples R China 3.Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China 4.Chinese Acad Sci CASIA, Inst Automat, Res Ctr Brain Inspired Intelligence, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Jia, Shuncheng,Zhang, Tielin,Cheng, Xiang,et al. Neuronal-Plasticity and Reward-Propagation Improved Recurrent Spiking Neural Networks[J]. FRONTIERS IN NEUROSCIENCE,2021,15:11.
APA	Jia, Shuncheng,Zhang, Tielin,Cheng, Xiang,Liu, Hongxing,&Xu, Bo.(2021).Neuronal-Plasticity and Reward-Propagation Improved Recurrent Spiking Neural Networks.FRONTIERS IN NEUROSCIENCE,15,11.
MLA	Jia, Shuncheng,et al."Neuronal-Plasticity and Reward-Propagation Improved Recurrent Spiking Neural Networks".FRONTIERS IN NEUROSCIENCE 15(2021):11.