Temporal-Sequential Learning With a Brain-Inspired Spiking Neural Network and Its Application to Musical Memory | |
Liang, Qian1,2; Zeng, Yi1,2,3,4; Xu, Bo1,2,3 | |
刊名 | FRONTIERS IN COMPUTATIONAL NEUROSCIENCE |
2020-07-02 | |
卷号 | 14期号:0页码:51 |
关键词 | spiking neural network sequential memory episodic memory spike-timing-dependent plasticity time perception musical learning |
DOI | 10.3389/fncom.2020.00051 |
英文摘要 | Sequence learning is a fundamental cognitive function of the brain. However, the ways in which sequential information is represented and memorized are not dealt with satisfactorily by existing models. To overcome this deficiency, this paper introduces a spiking neural network based on psychological and neurobiological findings at multiple scales. Compared with existing methods, our model has four novel features: (1) It contains several collaborative subnetworks similar to those in brain regions with different cognitive functions. The individual building blocks of the simulated areas are neural functional minicolumns composed of biologically plausible neurons. Both excitatory and inhibitory connections between neurons are modulated dynamically using a spike-timing-dependent plasticity learning rule. (2) Inspired by the mechanisms of the brain's cortical-striatal loop, a dependent timing module is constructed to encode temporal information, which is essential in sequence learning but has not been processed well by traditional algorithms. (3) Goal-based and episodic retrievals can be achieved at different time scales. (4) Musical memory is used as an application to validate the model. Experiments show that the model can store a huge amount of data on melodies and recall them with high accuracy. In addition, it can remember the entirety of a melody given only an episode or the melody played at different paces. |
资助项目 | Strategic Priority Research Program of the Chinese Academy of Sciences[XDB32070100] ; Beijing Municipality of Science and Technology[Z181100001518006] ; Major Research Program of Shandong Province[2018CXGC1503] ; Beijing Natural Science Foundation[4184103] ; National Natural Science Foundation of China[61806195] ; Beijing Academy of Artificial Intelligence (BAAI) |
WOS关键词 | RECEPTIVE-FIELDS ; EFFERENT NEURONS ; PREMOTOR CORTEX ; TIME CELLS ; INTERVAL ; HIPPOCAMPUS ; PERCEPTION ; SEQUENCES ; PREDICTION ; MODEL |
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:000553063100001 |
资助机构 | Strategic Priority Research Program of the Chinese Academy of Sciences ; Beijing Municipality of Science and Technology ; Major Research Program of Shandong Province ; Beijing Natural Science Foundation ; National Natural Science Foundation of China ; Beijing Academy of Artificial Intelligence (BAAI) |
内容类型 | 期刊论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/40224] |
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 Artificial Intelligence, Beijing, Peoples R China 2.Chinese Acad Sci, Res Ctr Brain Inspired Intelligence, Inst Automat, 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 |
推荐引用方式 GB/T 7714 | Liang, Qian,Zeng, Yi,Xu, Bo. Temporal-Sequential Learning With a Brain-Inspired Spiking Neural Network and Its Application to Musical Memory[J]. FRONTIERS IN COMPUTATIONAL NEUROSCIENCE,2020,14(0):51. |
APA | Liang, Qian,Zeng, Yi,&Xu, Bo.(2020).Temporal-Sequential Learning With a Brain-Inspired Spiking Neural Network and Its Application to Musical Memory.FRONTIERS IN COMPUTATIONAL NEUROSCIENCE,14(0),51. |
MLA | Liang, Qian,et al."Temporal-Sequential Learning With a Brain-Inspired Spiking Neural Network and Its Application to Musical Memory".FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 14.0(2020):51. |
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