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Brain Inspired Sequences Production by Spiking Neural Networks With Reward-Modulated STDP | |
Fang, Hongjian1,2![]() ![]() ![]() | |
发表期刊 | FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
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2021-02-16 | |
卷号 | 15页码:13 |
产权排序 | 1 |
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. |
关键词 | brain-inspired intelligence spiking neural network reward-medulated STDP population coding reinforcement learning |
DOI | 10.3389/fncom.2021.612041 |
收录类别 | SCI |
语种 | 英语 |
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) |
项目资助者 | 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) |
WOS研究方向 | Mathematical & Computational Biology ; Neurosciences & Neurology |
WOS类目 | Mathematical & Computational Biology ; Neurosciences |
WOS记录号 | WOS:000624066200001 |
出版者 | FRONTIERS MEDIA SA |
七大方向——子方向分类 | 类脑模型与计算 |
国重实验室规划方向分类 | 认知机理与类脑学习 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/43354 |
专题 | 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, 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 |
第一作者单位 | 类脑智能研究中心 |
通讯作者单位 | 类脑智能研究中心; 模式识别国家重点实验室 |
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. |
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SPSNN_pub.pdf(2485KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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