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manmenghua

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请帮忙检索文章的SCI收录号，文章名：Quantification of degeneracy in Hodgkin-Huxley neurons on newman-watts small world network.作者Menghua Man,杂志：Journal of theoretical biology.2016,402C,67-74

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Quantification of degeneracy in Hodgkin-Huxley neurons on Newman-Watts small world network
作者:Man, MH (Man, Menghua)[ 1 ] ; Zhang, Y (Zhang, Ya)[ 1 ] ; Ma, GL (Ma, Guilei)[ 1 ] ; Friston, K (Friston, Karl)[ 2 ] ; Liu, SH (Liu, Shanghe)[ 1 ]
JOURNAL OF THEORETICAL BIOLOGY
卷: 402  頁碼: 62-74
DOI: 10.1016/j.jtbi.2016.05.004
出版日期: AUG 7 2016
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2楼: Originally posted by FMStation at 2016-08-14 12:55:46
Quantification of degeneracy in Hodgkin-Huxley neurons on Newman-Watts small world network
作者:Man, MH (Man, Menghua) ; Zhang, Y (Zhang, Ya) ; Ma, GL (Ma, Guilei) ; Friston, K (Friston, Karl) ; Liu ...

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manmenghua(lazy锦溪代发): 金币+20, 协助结帖，感谢应助！ 2016-08-17 15:46:55
lazy锦溪: LS-EPI+1 2016-08-17 15:47:14
Quantification of degeneracy in Hodgkin-Huxley neurons on Newman-Watts small world network
作者:Man, MH (Man, Menghua)[ 1 ] ; Zhang, Y (Zhang, Ya)[ 1 ] ; Ma, GL (Ma, Guilei)[ 1 ] ; Friston, K (Friston, Karl)[ 2 ] ; Liu, SH (Liu, Shanghe)[ 1 ]


JOURNAL OF THEORETICAL BIOLOGY


卷: 402
页: 62-74
DOI: 10.1016/j.jtbi.2016.05.004

出版年: AUG 7 2016

查看期刊信息

JOURNAL OF THEORETICAL BIOLOGY  

出版商 ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD, 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND

ISSN: 0022-5193
eISSN: 1095-8541

研究领域 Life Sciences & Biomedicine - Other Topics
Mathematical & Computational Biology



摘要
Degeneracy is a fundamental source of biological robustness, complexity and evolvability in many biological systems. However, degeneracy is often confused with redundancy. Furthermore, the quantification of degeneracy has not been addressed for realistic neuronal networks. The objective of this paper is to characterize degeneracy in neuronal network models via quantitative mathematic measures. Firstly, we establish Hodgkin-Huxley neuronal networks with Newman-Watts small world network architectures. Secondly, in order to calculate the degeneracy, redundancy and complexity in the ensuing networks, we use information entropy to quantify the information a neuronal response carries about the stimulus - and mutual information to measure the contribution of each subset of the neuronal network. Finally, we analyze the interdependency of degeneracy, redundancy and complexity and how these three measures depend upon network architectures. Our results suggest that degeneracy can be applied to any neuronal network as a formal measure, and degeneracy is distinct from redundancy. Qualitatively degeneracy and complexity are more highly correlated over different network architectures, in comparison to redundancy. Quantitatively, the relationship between both degeneracy and redundancy depends on network coupling strength: both degeneracy and redundancy increase with complexity for small coupling strengths; however, as coupling strength increases, redundancy decreases with complexity (in contrast to degeneracy, which is relatively invariant). These results suggest that the degeneracy is a general topologic characteristic of neuronal networks, which could be applied quantitatively in neuroscience and connectomics. (C) 2016 Elsevier Ltd. All rights reserved.

关键词
作者关键词:Complexity; Redundancy; Neuronal networks

KeyWords Plus:NON-GAUSSIAN NOISE; FUNCTIONAL CONNECTIVITY; MULTIPLE RESONANCES; COGNITIVE FUNCTIONS; TIME DELAYS; BRAIN; COMPLEXITY; SYSTEMS; INFORMATION; MODEL

作者信息
通讯作者地址: Man, MH (通讯作者)       Mech Engn Coll, Electrostat & Electromagnet Protect Inst, Shijiazhuang, Peoples R China.


地址:        [ 1 ] Mech Engn Coll, Electrostat & Electromagnet Protect Inst, Shijiazhuang, Peoples R China
[ 2 ] UCL, Inst Neurol, Wellcome Trust Ctr Neuroimaging, Queen Sq, London, England
  增强组织信息的名称
    University College London
    University of London  


电子邮件地址:manmenghua@126.com

基金资助致谢
基金资助机构 授权号
National Natural Science Foundation of China  51407194  
Wellcome Trust  088130/Z/09/Z  
查看基金资助信息关闭基金资助信息   

Thank anonymous reviewers for constructive comments on this work. We would like to thank Mai Lu and Qian Zhou for helpful suggestions. This work was supported by the National Natural Science Foundation of China (Grant no. 51407194). K.F. is funded by the Wellcome Trust (Ref.: 088130/Z/09/Z).

出版商
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD, 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND

类别 / 分类
研究方向:Life Sciences & Biomedicine - Other Topics; Mathematical & Computational Biology

Web of Science 类别:Biology; Mathematical & Computational Biology

文献信息
文献类型:Article

语种:English

入藏号: WOS:000377623700008

PubMed ID: 27155043

ISSN: 0022-5193

eISSN: 1095-8541

其他信息
IDS 号: DO2QA

Web of Science 核心合集中的 "引用的参考文献": 73

Web of Science 核心合集中的 "被引频次": 0

影响因子 2.049  2.156  
2015  5 年  


JCR® 类别 类别中的排序 JCR 分区
BIOLOGY  25/86  Q2
MATHEMATICAL & COMPUTATIONAL BIOLOGY  14/56  Q1


数据来自第 2015 版 Journal Citation Reports®
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https://dx.doi.org/10.1016/j.jtbi.2016.05.004
0022-5193
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