Adaptive structure evolution and biologically plausible synaptic plasticity for recurrent spiking neural networks

doi:10.1038/s41598-023-43488-x

CASIA OpenIR > 脑图谱与类脑智能实验室

	Adaptive structure evolution and biologically plausible synaptic plasticity for recurrent spiking neural networks
	Pan, Wenxuan 1,2; Zhao, Feifei1 ; Zeng, Yi1,2,3,4 ; Han, Bing 1,2
发表期刊	SCIENTIFIC REPORTS
ISSN	2045-2322
	2023-10-07
卷号	13 期号:1 页码:13
通讯作者	Zeng, Yi(yi.zeng@ia.ac.cn)
摘要	The architecture design and multi-scale learning principles of the human brain that evolved over hundreds of millions of years are crucial to realizing human-like intelligence. Spiking neural network based Liquid State Machine (LSM) serves as a suitable architecture to study brain-inspired intelligence because of its brain-inspired structure and the potential for integrating multiple biological principles. Existing researches on LSM focus on different certain perspectives, including high-dimensional encoding or optimization of the liquid layer, network architecture search, and application to hardware devices. There is still a lack of in-depth inspiration from the learning and structural evolution mechanism of the brain. Considering these limitations, this paper presents a novel LSM learning model that integrates adaptive structural evolution and multi-scale biological learning rules. For structural evolution, an adaptive evolvable LSM model is developed to optimize the neural architecture design of liquid layer with separation property. For brain-inspired learning of LSM, we propose a dopamine-modulated Bienenstock-Cooper-Munros (DA-BCM) method that incorporates global long-term dopamine regulation and local trace-based BCM synaptic plasticity. Comparative experimental results on different decision-making tasks show that introducing structural evolution of the liquid layer, and the DA-BCM regulation of the liquid layer and the readout layer could improve the decision-making ability of LSM and flexibly adapt to rule reversal. This work is committed to exploring how evolution can help to design more appropriate network architectures and how multi-scale neuroplasticity principles coordinated to enable the optimization and learning of LSMs for relatively complex decision-making tasks.
DOI	10.1038/s41598-023-43488-x
关键词[WOS]	BRAIN ; ARCHITECTURE ; PREDICTION ; FRAMEWORK ; MODEL ; COST
收录类别	SCI
语种	英语
资助项目	This work is supported by the National Key Research and Development Program (Grant No. 2020AAA0107800), the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB32070100), the National Natural Science Foundation of China (Gr[2020AAA0107800] ; National Key Research and Development Program[XDB32070100] ; Strategic Priority Research Program of the Chinese Academy of Sciences[62106261] ; National Natural Science Foundation of China
项目资助者	This work is supported by the National Key Research and Development Program (Grant No. 2020AAA0107800), the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB32070100), the National Natural Science Foundation of China (Gr ; National Key Research and Development Program ; Strategic Priority Research Program of the Chinese Academy of Sciences ; National Natural Science Foundation of China
WOS研究方向	Science & Technology - Other Topics
WOS类目	Multidisciplinary Sciences
WOS记录号	WOS:001084389900030
出版者	NATURE PORTFOLIO
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/54314
专题	脑图谱与类脑智能实验室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, Brain Inspired Cognit Intelligence Lab, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Sch Future Technol, Beijing, Peoples R China 4.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai, Peoples R China
第一作者单位	中国科学院自动化研究所
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	Pan, Wenxuan,Zhao, Feifei,Zeng, Yi,et al. Adaptive structure evolution and biologically plausible synaptic plasticity for recurrent spiking neural networks[J]. SCIENTIFIC REPORTS,2023,13(1):13.
APA	Pan, Wenxuan,Zhao, Feifei,Zeng, Yi,&Han, Bing.(2023).Adaptive structure evolution and biologically plausible synaptic plasticity for recurrent spiking neural networks.SCIENTIFIC REPORTS,13(1),13.
MLA	Pan, Wenxuan,et al."Adaptive structure evolution and biologically plausible synaptic plasticity for recurrent spiking neural networks".SCIENTIFIC REPORTS 13.1(2023):13.