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Brain Inspired Sequences Production by Spiking Neural Networks With Reward-Modulated STDP
Fang, Hongjian1,2; Zeng, Yi1,2,3,4; Zhao, Feifei1
Source PublicationFRONTIERS IN COMPUTATIONAL NEUROSCIENCE
2021-02-16
Volume15Pages:13
Corresponding AuthorZeng, Yi(yi.zeng@ia.ac.cn)
AbstractUnderstanding 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.
Keywordbrain-inspired intelligence spiking neural network reward-medulated STDP population coding reinforcement learning
DOI10.3389/fncom.2021.612041
Indexed BySCI
Language英语
Funding ProjectStrategic 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)
Funding OrganizationStrategic 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)
WOS Research AreaMathematical & Computational Biology ; Neurosciences & Neurology
WOS SubjectMathematical & Computational Biology ; Neurosciences
WOS IDWOS:000624066200001
PublisherFRONTIERS MEDIA SA
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/43354
Collection类脑智能研究中心_类脑认知计算
Corresponding AuthorZeng, Yi
Affiliation1.Chinese Acad Sci, Inst Automat, Res Ctr Brain Inspired Intelligence, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Sch Future Technol, Beijing, Peoples R China
3.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai, Peoples R China
4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
First Author Affilication类脑智能研究中心
Corresponding Author Affilication类脑智能研究中心;  Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
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.
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