中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 复杂系统认知与决策实验室
Origin of the efficiency of spike timing-based neural computation for processing temporal information

文献类型：期刊论文

作者Jiang, Zhiwei2,4; Xu, Jiaming2,3; Zhang, Tielin2,3; Poo, Mu-ming1,2,4; Xu, Bo2,3
刊名NEURAL NETWORKS
出版日期2023-03-01
卷号160页码:84-96
ISSN号0893-6080
关键词Postsynaptic potential Spiking neural network Sequence learning Temporal information processing
DOI10.1016/j.neunet.2022.12.017
通讯作者Poo, Mu-ming(mpoo@ion.ac.cn) ; Xu, Bo(xubo@ia.ac.cn)
英文摘要Although the advantage of spike timing-based over rate-based network computation has been recog-nized, the underlying mechanism remains unclear. Using Tempotron and Perceptron as elementary neural models, we examined the intrinsic difference between spike timing-based and rate-based computations. For more direct comparison, we modified Tempotron computation into rate-based computation with the retention of some temporal information. Previous studies have shown that spike timing-based computation are computationally more powerful than rate-based computation in terms of the number of computational units required and the capability in classifying random patterns. Our study showed that spike timing-based and rate-based Tempotron computations provided similar capability in classifying random spike patterns, as well as in text sentiment classification and spam text detection. However, spike timing-based computation is superior in performing a task involving discriminating forward vs. reverse sequence of events, i.e., information mainly temporal in nature. Further studies revealed that this superiority required the asymmetry in the profile of the postsynaptic potential (PSP), and that temporal sequence information was converted to biased spatial distribution of synaptic weight modifications during learning. Thus, the intrinsic PSP asymmetry is a mechanistic basis for the high efficiency of spike timing-based computation for processing temporal information.(c) 2022 Elsevier Ltd. All rights reserved.
WOS关键词NETWORKS ; PLASTICITY ; NEURONS ; CODE
资助项目Key Research Program of Frontier Sciences, China, Chinese Academy of Sciences, China[QYZDY-SSW-SMCO01] ; Strategic Priority Research Program of Chinese Academy of Sciences, China[XDB32070100] ; International Partnership Program of Chinese Academy of Sciences, China[153D31KYSB20170059] ; Shanghai Municipal Science and Technology Major Project, China[2018SHZDZX05] ; Shanghai Key Basic Research Project, China[16JC1420201] ; Shanghai Key Basic Research Project, China[18JC1410100]
WOS研究方向Computer Science ; Neurosciences & Neurology
语种英语
出版者PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号WOS:000923418600001
资助机构Key Research Program of Frontier Sciences, China, Chinese Academy of Sciences, China ; Strategic Priority Research Program of Chinese Academy of Sciences, China ; International Partnership Program of Chinese Academy of Sciences, China ; Shanghai Municipal Science and Technology Major Project, China ; Shanghai Key Basic Research Project, China
源URL[http://ir.ia.ac.cn/handle/173211/51413]  
专题复杂系统认知与决策实验室
通讯作者Poo, Mu-ming; Xu, Bo
作者单位1.Shanghai Ctr Brain Sci & Brain Inspired Intelligen, Lingang Lab, Shanghai 200031, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
4.Chinese Acad Sci, Inst Neurosci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai 200031, Peoples R China
推荐引用方式
GB/T 7714		 Jiang, Zhiwei,Xu, Jiaming,Zhang, Tielin,et al. Origin of the efficiency of spike timing-based neural computation for processing temporal information[J]. NEURAL NETWORKS,2023,160:84-96.
APA Jiang, Zhiwei,Xu, Jiaming,Zhang, Tielin,Poo, Mu-ming,&Xu, Bo.(2023).Origin of the efficiency of spike timing-based neural computation for processing temporal information.NEURAL NETWORKS,160,84-96.
MLA Jiang, Zhiwei,et al."Origin of the efficiency of spike timing-based neural computation for processing temporal information".NEURAL NETWORKS 160(2023):84-96.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。