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, Guanxiong2,3,4 ; Huang, Xuhui2,4,5 ; Jiang, Tianzi1,2,3,4 ; Yu, Shan1,2,3,4
刊名	NEURAL NETWORKS
	2019-10-01
卷号	118 页码:140-147
关键词	Reservoir computing Sequence learning and retrieval Short-term depression Synaptic heterogeneity Self-organized criticality Optimal information processing
ISSN号	0893-6080
DOI	10.1016/j.neunet.2019.06.002
通讯作者	Huang, Xuhui(xuhui.huang@ia.ac.cn) ; Yu, Shan(shan.yu@nlpr.ia.ac.cn)
英文摘要	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.
资助项目	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]
WOS关键词	SELF-ORGANIZED CRITICALITY ; NEURONAL AVALANCHES ; CORTICAL NETWORKS ; SYNAPSES ; COMPUTATION ; DEPRESSION ; CAPACITY ; CHAOS ; EDGE
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:000483920500012
资助机构	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
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/27196]
专题	中国科学院自动化研究所
通讯作者	Huang, Xuhui; Yu, Shan
作者单位	1.Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Brainnetome Ctr, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China 5.Chinese Acad Sci, Inst Automat, Res Ctr Brain Inspired Intelligence, Beijing 100190, Peoples R China
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.