NeuroElectro: a window to the world's neuron electrophysiology data.
NeuroElectro: a window to the world's neuron electrophysiology data.
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DOI：
10.3389/fninf.2014.00040
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发表时间：
2014
影响因子：
3.5
通讯作者：
Gerkin RC
中科院分区：
文献类型：
--
作者：
Tripathy SJ;Savitskaya J;Burton SD;Urban NN;Gerkin RC
神经回路的行为在很大程度上取决于它们所包含的神经元的电生理特性。要了解这些属性之间的关系，需要首先识别每个属性并对其进行分类。然而，有关这些属性的信息很大程度上被封锁在几十年的封闭访问期刊文章中，报告结果的约定各不相同，这使得利用底层数据变得困难。我们通过NeuroElectro项目解决了这个问题：一个PYTHON库、RESTful API和Web应用程序(位于http://neuroelectro.org))，用于提取、可视化和总结已公布的神经元电生理特性数据。信息既按神经元类型(使用NeuroLex提供的神经元定义)组织，也按电生理属性(使用新开发的本体)组织。我们描述了与从期刊文章中自动提取表格电生理数据和方法元数据相关的技术和挑战。我们将进一步讨论如何最好地组合、标准化和组织跨这些异构源的数据的策略。对于试图补充他们自己的数据的实验生理学家、希望限制他们的模型参数的计算建模师以及寻找神经元及其特性之间未发现的关系的理论家来说，神经电学是一个宝贵的资源。
The behavior of neural circuits is determined largely by the electrophysiological properties of the neurons they contain. Understanding the relationships of these properties requires the ability to first identify and catalog each property. However, information about such properties is largely locked away in decades of closed-access journal articles with heterogeneous conventions for reporting results, making it difficult to utilize the underlying data. We solve this problem through the NeuroElectro project: a Python library, RESTful API, and web application (at http://neuroelectro.org) for the extraction, visualization, and summarization of published data on neurons' electrophysiological properties. Information is organized both by neuron type (using neuron definitions provided by NeuroLex) and by electrophysiological property (using a newly developed ontology). We describe the techniques and challenges associated with the automated extraction of tabular electrophysiological data and methodological metadata from journal articles. We further discuss strategies for how to best combine, normalize and organize data across these heterogeneous sources. NeuroElectro is a valuable resource for experimental physiologists attempting to supplement their own data, for computational modelers looking to constrain their model parameters, and for theoreticians searching for undiscovered relationships among neurons and their properties.
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影响因子：
34.7
作者：
Ascoli, Giorgio A.;Alonso-Nanclares, Lidia;Anderson, Stewart A.;Barrionuevo, German;Benavides-Piccione, Ruth;Burkhalter, Andreas;Buzsaki, Gyoergy;Cauli, Bruno;DeFelipe, Javier;Fairen, Alfonso;Feldmeyer, Dirk;Fishell, Gord;Fregnac, Yves;Freund, Tamas F.;Gardner, Daniel;Gardner, Esther P.;Goldberg, Jesse H.;Helmstaedter, Moritz;Hestrin, Shaul;Karube, Fuyuki;Kisvarday, Zoltan F.;Lambolez, Bertrand;Lewis, David A.;Marin, Oscar;Markram, Henry;Munoz, Alberto;Packer, Adam;Petersen, Carl C. H.;Rockland, Kathleen S.;Rossier, Jean;Rudy, Bernardo;Somogyi, Peter;Staiger, Jochen F.;Tamas, Gabor;Thomson, Alex M.;Toledo-Rodriguez, Maria;Wang, Yun;West, David C.;Yuste, Rafael
通讯作者：
Yuste, Rafael
影响因子：
--
作者：
Ramakrishnan C;Patnia A;Hovy E;Burns GA
通讯作者：
Burns GA
影响因子：
4.3
作者：
French L;Pavlidis P
通讯作者：
Pavlidis P
影响因子：
3.5
作者：
Larson SD;Martone ME
通讯作者：
Martone ME
影响因子：
1.2
作者：
Hines, ML;Morse, T;Shepherd, GM
通讯作者：
Shepherd, GM