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

文献类型：期刊论文

作者Pan, Wenxuan3,4; Zhao, Feifei4; Zeng, Yi1,2,3,4; Han, Bing3,4
刊名SCIENTIFIC REPORTS
出版日期2023-10-07
卷号13期号:1页码:13
ISSN号2045-2322
DOI10.1038/s41598-023-43488-x
通讯作者Zeng, Yi(yi.zeng@ia.ac.cn)
英文摘要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.
WOS关键词BRAIN ; ARCHITECTURE ; PREDICTION ; FRAMEWORK ; MODEL ; COST
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
WOS研究方向Science & Technology - Other Topics
语种英语
出版者NATURE PORTFOLIO
WOS记录号WOS:001084389900030
资助机构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
源URL[http://ir.ia.ac.cn/handle/173211/54313]  
专题脑图谱与类脑智能实验室
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.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai, Peoples R China
2.Univ Chinese Acad Sci, Sch Future Technol, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
4.Chinese Acad Sci, Inst Automat, Brain Inspired Cognit Intelligence Lab, Beijing, Peoples R China
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.

入库方式： OAI收割

来源：自动化研究所

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