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manmenghua金虫 (著名写手)
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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 发自小木虫Android客户端 |
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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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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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