Short-term synaptic plasticity expands the operational range of long-term synaptic changes in neural networks

doi:10.1016/j.neunet.2019.06.002

	Short-term synaptic plasticity expands the operational range of long-term synaptic changes in neural networks
	Zeng, Guanxiong1,2,5 ; Huang, Xuhui1,2,3 ; Jiang, Tianzi1,2,4,5 ; Yu, Shan1,2,4,5
发表期刊	NEURAL NETWORKS
ISSN	0893-6080
	2019-10-01
卷号	118 页码:140-147
通讯作者	Huang, Xuhui(xuhui.huang@ia.ac.cn) ; Yu, Shan(shan.yu@nlpr.ia.ac.cn)
摘要	The brain is highly plastic, with synaptic weights changing across a wide range of time scales, from hundreds of milliseconds to days. Changes occurring at different temporal scales are believed to serve different purposes, with long-term changes for learning and memory and short-term changes for adaptation and synaptic computation. By studying the performance of reservoir computing (RC) models in a memory task, we revealed that short-term synaptic plasticity is fundamentally important for long-term synaptic changes in neural networks. Specifically, short-term depression (STD) greatly expands the operational range of a neural network in which it can accommodate long-term synaptic changes while maintaining system performance. This is achieved by dynamically adjusting neural networks close to a critical state. The effects of STD can be further strengthened by synaptic weight heterogeneity, resulting in networks that can tolerate very large, long-term changes in synaptic weights. Our results highlight a potential mechanism used by the brain to organize plasticity at different time scales, thereby maintaining optimal information processing while allowing internal structural changes necessary for learning and memory. (C) 2019 Elsevier Ltd. All rights reserved.
关键词	Reservoir computing Sequence learning and retrieval Short-term depression Synaptic heterogeneity Self-organized criticality Optimal information processing
DOI	10.1016/j.neunet.2019.06.002
关键词[WOS]	SELF-ORGANIZED CRITICALITY ; NEURONAL AVALANCHES ; CORTICAL NETWORKS ; SYNAPSES ; COMPUTATION ; DEPRESSION ; CAPACITY ; CHAOS ; EDGE
收录类别	SCI
语种	英语
资助项目	Key Research Program of Frontier Sciences, CAS[QYZDJ-SSW-SMC019] ; Hundred-Talent Program of CAS ; Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDB32030200] ; Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDB32040200] ; Natural Science Foundation of China[31620103905] ; Natural Science Foundation of China[91732305] ; Natural Science Foundation of China[11505283] ; Natural Science Foundation of China[81471368] ; National Key Research and Development Program of China[2017YFA0105203] ; National Key Research and Development Program of China[2017YFA0105203] ; Natural Science Foundation of China[81471368] ; Natural Science Foundation of China[11505283] ; Natural Science Foundation of China[91732305] ; Natural Science Foundation of China[31620103905] ; Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDB32040200] ; Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDB32030200] ; Hundred-Talent Program of CAS ; Key Research Program of Frontier Sciences, CAS[QYZDJ-SSW-SMC019]
项目资助者	National Key Research and Development Program of China ; Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences (CAS) ; Hundred-Talent Program of CAS ; Key Research Program of Frontier Sciences, CAS
WOS研究方向	Computer Science ; Neurosciences & Neurology
WOS类目	Computer Science, Artificial Intelligence ; Neurosciences
WOS记录号	WOS:000483920500012
出版者	PERGAMON-ELSEVIER SCIENCE LTD
七大方向——子方向分类	类脑模型与计算
引用统计	被引频次：7[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/27197
专题	脑图谱与类脑智能实验室_脑网络组研究
通讯作者	Huang, Xuhui; Yu, Shan
作者单位	1.Chinese Acad Sci, Brainnetome Ctr, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Inst Automat, Res Ctr Brain Inspired Intelligence, Beijing 100190, Peoples R China 4.Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China 5.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
第一作者单位	模式识别国家重点实验室
通讯作者单位	模式识别国家重点实验室; 中国科学院自动化研究所
推荐引用方式 GB/T 7714	Zeng, Guanxiong,Huang, Xuhui,Jiang, Tianzi,et al. Short-term synaptic plasticity expands the operational range of long-term synaptic changes in neural networks[J]. NEURAL NETWORKS,2019,118:140-147.
APA	Zeng, Guanxiong,Huang, Xuhui,Jiang, Tianzi,&Yu, Shan.(2019).Short-term synaptic plasticity expands the operational range of long-term synaptic changes in neural networks.NEURAL NETWORKS,118,140-147.
MLA	Zeng, Guanxiong,et al."Short-term synaptic plasticity expands the operational range of long-term synaptic changes in neural networks".NEURAL NETWORKS 118(2019):140-147.