Brain Inspired Sequences Production by Spiking Neural Networks With Reward-Modulated STDP

doi:10.3389/fncom.2021.612041

CORC > 自动化研究所 > 中国科学院自动化研究所 > 类脑智能研究中心 > 类脑认知计算

	Brain Inspired Sequences Production by Spiking Neural Networks With Reward-Modulated STDP
	Fang, Hongjian 1,3; Zeng, Yi 1,2,3,4; Zhao, Feifei 3
刊名	FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
	2021-02-16
卷号	15 页码:13
关键词	brain-inspired intelligence spiking neural network reward-medulated STDP population coding reinforcement learning
DOI	10.3389/fncom.2021.612041
通讯作者	Zeng, Yi(yi.zeng@ia.ac.cn)
英文摘要	Understanding and producing embedded sequences according to supra-regular grammars in language has always been considered a high-level cognitive function of human beings, named "syntax barrier" between humans and animals. However, some neurologists recently showed that macaques could be trained to produce embedded sequences involving supra-regular grammars through a well-designed experiment paradigm. Via comparing macaques and preschool children's experimental results, they claimed that human uniqueness might only lie in the speed and learning strategy resulting from the chunking mechanism. Inspired by their research, we proposed a Brain-inspired Sequence Production Spiking Neural Network (SP-SNN) to model the same production process, followed by memory and learning mechanisms of the multi-brain region cooperation. After experimental verification, we demonstrated that SP-SNN could also handle embedded sequence production tasks, striding over the "syntax barrier." SP-SNN used Population-Coding and STDP mechanism to realize working memory, Reward-Modulated STDP mechanism for acquiring supra-regular grammars. Therefore, SP-SNN needs to simultaneously coordinate short-term plasticity (STP) and long-term plasticity (LTP) mechanisms. Besides, we found that the chunking mechanism indeed makes a difference in improving our model's robustness. As far as we know, our work is the first one toward the "syntax barrier" in the SNN field, providing the computational foundation for further study of related underlying animals' neural mechanisms in the future.
资助项目	Strategic Priority Research Program of the Chinese Academy of Sciences[XDB32070100] ; new generation of artificial intelligencemajor project of the Ministry of Science and Technology of the People's Republic of China[2020AAA0104305] ; Beijing Municipal Commission of Science and Technology[Z181100001518006] ; Beijing Academy of Artificial Intelligence (BAAI)
WOS研究方向	Mathematical & Computational Biology ; Neurosciences & Neurology
语种	英语
出版者	FRONTIERS MEDIA SA
WOS记录号	WOS:000624066200001
资助机构	Strategic Priority Research Program of the Chinese Academy of Sciences ; new generation of artificial intelligencemajor project of the Ministry of Science and Technology of the People's Republic of China ; Beijing Municipal Commission of Science and Technology ; Beijing Academy of Artificial Intelligence (BAAI)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/43353]
专题	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.Univ Chinese Acad Sci, Sch Future Technol, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Automat, Res Ctr Brain Inspired Intelligence, Beijing, Peoples R China 4.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai, Peoples R China
推荐引用方式 GB/T 7714	Fang, Hongjian,Zeng, Yi,Zhao, Feifei. Brain Inspired Sequences Production by Spiking Neural Networks With Reward-Modulated STDP[J]. FRONTIERS IN COMPUTATIONAL NEUROSCIENCE,2021,15:13.
APA	Fang, Hongjian,Zeng, Yi,&Zhao, Feifei.(2021).Brain Inspired Sequences Production by Spiking Neural Networks With Reward-Modulated STDP.FRONTIERS IN COMPUTATIONAL NEUROSCIENCE,15,13.
MLA	Fang, Hongjian,et al."Brain Inspired Sequences Production by Spiking Neural Networks With Reward-Modulated STDP".FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 15(2021):13.