Modeling learnable electrical synapse for high precision spatio-temporal recognition

doi:10.1016/j.neunet.2022.02.006

	Modeling learnable electrical synapse for high precision spatio-temporal recognition
	Wu, Zhenzhi 1; Zhang, Zhihong 2; Gao, Huanhuan 2; Qin, Jun 2; Zhao, Rongzhen 1; Zhao, Guangshe 2; Li, Guoqi 3,4
发表期刊	NEURAL NETWORKS
ISSN	0893-6080
	2022-05-01
卷号	149 页码:184-194
通讯作者	Li, Guoqi(guoqi.li@ia.ac.cn)
摘要	Bio-inspired recipes are being introduced to artificial neural networks for the efficient processing of spatio-temporal tasks. Among them, Leaky Integrate and Fire (LIF) model is the most remarkable one thanks to its temporal processing capability, lightweight model structure, and well investigated direct training methods. However, most learnable LIF networks generally take neurons as independent individuals that communicate via chemical synapses, leaving electrical synapses all behind. On the contrary, it has been well investigated in biological neural networks that the inter-neuron electrical synapse takes a great effect on the coordination and synchronization of generating action potentials. In this work, we are engaged in modeling such electrical synapses in artificial LIF neurons, where membrane potentials propagate to neighbor neurons via convolution operations, and the refined neural model ECLIF is proposed. We then build deep networks using ECLIF and trained them using a back-propagation-through-time algorithm. We found that the proposed network has great accuracy improvement over traditional LIF on five datasets and achieves high accuracy on them. In conclusion, it reveals that the introduction of the electrical synapse is an important factor for achieving high accuracy on realistic spatio-temporal tasks.
关键词	Electrical synapse coupling Leaky-integrate-and-fire model Spatio-temporal information Bio-plausible neuronal dynamics
DOI	10.1016/j.neunet.2022.02.006
关键词[WOS]	SPIKING NEURONS ; FIRE MODEL ; BACKPROPAGATION ; MECHANISMS ; NETWORKS ; DYNAMICS
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Neurosciences & Neurology
WOS类目	Computer Science, Artificial Intelligence ; Neurosciences
WOS记录号	WOS:000793060100014
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计	被引频次：3[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/173211/49402
专题	复杂系统认知与决策实验室_听觉模型与认知计算
通讯作者	Li, Guoqi
作者单位	1.Lynxi Technol, Beijing 100097, Peoples R China 2.Xi An Jiao Tong Univ, Sch Automation Sci & Engn, Xian 710049, Shaanxi, Peoples R China 3.Chinese Acad Sci, Inst Automaton, Beijing 100190, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Wu, Zhenzhi,Zhang, Zhihong,Gao, Huanhuan,et al. Modeling learnable electrical synapse for high precision spatio-temporal recognition[J]. NEURAL NETWORKS,2022,149:184-194.
APA	Wu, Zhenzhi.,Zhang, Zhihong.,Gao, Huanhuan.,Qin, Jun.,Zhao, Rongzhen.,...&Li, Guoqi.(2022).Modeling learnable electrical synapse for high precision spatio-temporal recognition.NEURAL NETWORKS,149,184-194.
MLA	Wu, Zhenzhi,et al."Modeling learnable electrical synapse for high precision spatio-temporal recognition".NEURAL NETWORKS 149(2022):184-194.