NeuroML: a language for describing data driven models of neurons and networks with a high degree of biological detail.
NeuroML: a language for describing data driven models of neurons and networks with a high degree of biological detail.
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DOI：
10.1371/journal.pcbi.1000815
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发表时间：
2010-06-17
影响因子：
4.3
通讯作者：
Silver RA
中科院分区：
文献类型：
--
作者：
Gleeson P;Crook S;Cannon RC;Hines ML;Billings GO;Farinella M;Morse TM;Davison AP;Ray S;Bhalla US;Barnes SR;Dimitrova YD;Silver RA
生物学上详细的单神经元和网络模型对于理解离子通道、突触和解剖学连接如何构成大脑复杂电行为的基础非常重要。虽然神经元模拟器（如NEURON、GENESIS、MOOSE、NEST和PSICS）促进了这些数据驱动神经元模型的开发，但它们使用的专用语言通常不能互操作，限制了模型的可访问性，并阻止了模型组件的重用和交叉模拟器验证。为了克服这些问题，我们使用了开源软件的方法来开发NeuroML，基于XML（可扩展标记语言）的神经元模型描述语言。这使得这些详细的模型及其组件能够以独立的形式定义，允许它们在多个模拟器中使用并以标准化格式存档。在这里，我们描述了NeuroML的结构，并通过转换成NeuroML模型的一些不同的电压和配体门控电导，电耦合，突触传递和短期可塑性的模型，以及形态上详细的模型，个别神经元，展示其范围。我们还使用这些基于NeuroML的组件来开发高度详细的皮层网络模型。通过在五个独立开发的模拟器中展示类似的模型行为，验证了基于NeuroML的模型描述。虽然我们的研究结果证实，在不同的模拟器上运行的模拟收敛，他们揭示了模型互操作性的限制，通过显示，对于某些模型的收敛只发生在高水平的空间和时间离散化，当计算开销很高。我们开发的NeuroML作为生物解剖学详细的神经元和网络模型的通用描述语言，可以在多个模拟环境中实现互操作性，从而提高模型的透明度，可访问性和重用性。计算机建模正在成为研究大脑行为背后复杂相互作用的越来越有价值的工具。软件应用程序已经开发出来，这使得创建神经网络模型以及复制单个神经元电活动的详细模型变得更加容易。然而，这些应用程序使用的代码格式通常是不兼容的，这使得研究人员之间很难交换模型和想法。在这里，我们提出了一个神经元模型描述语言，NeuroML的结构。这提供了一种基于基础生理学以通用格式表达这些复杂模型的方法，允许它们映射到多个应用程序。我们通过将已发布的神经元模型转换为NeuroML格式并在许多常用模拟器上比较它们的行为来测试这种语言。创建一个通用的、可访问的模型描述格式将向更广泛的神经科学社区公开更多的模型细节，从而提高它们的质量和可靠性，就像其他开源软件一样。NeuroML还将允许开发一个更大的工具“生态系统”，用于构建、模拟和分析这些复杂的神经系统。
Biologically detailed single neuron and network models are important for understanding how ion channels, synapses and anatomical connectivity underlie the complex electrical behavior of the brain. While neuronal simulators such as NEURON, GENESIS, MOOSE, NEST, and PSICS facilitate the development of these data-driven neuronal models, the specialized languages they employ are generally not interoperable, limiting model accessibility and preventing reuse of model components and cross-simulator validation. To overcome these problems we have used an Open Source software approach to develop NeuroML, a neuronal model description language based on XML (Extensible Markup Language). This enables these detailed models and their components to be defined in a standalone form, allowing them to be used across multiple simulators and archived in a standardized format. Here we describe the structure of NeuroML and demonstrate its scope by converting into NeuroML models of a number of different voltage- and ligand-gated conductances, models of electrical coupling, synaptic transmission and short-term plasticity, together with morphologically detailed models of individual neurons. We have also used these NeuroML-based components to develop an highly detailed cortical network model. NeuroML-based model descriptions were validated by demonstrating similar model behavior across five independently developed simulators. Although our results confirm that simulations run on different simulators converge, they reveal limits to model interoperability, by showing that for some models convergence only occurs at high levels of spatial and temporal discretisation, when the computational overhead is high. Our development of NeuroML as a common description language for biophysically detailed neuronal and network models enables interoperability across multiple simulation environments, thereby improving model transparency, accessibility and reuse in computational neuroscience. Computer modeling is becoming an increasingly valuable tool in the study of the complex interactions underlying the behavior of the brain. Software applications have been developed which make it easier to create models of neural networks as well as detailed models which replicate the electrical activity of individual neurons. The code formats used by each of these applications are generally incompatible however, making it difficult to exchange models and ideas between researchers. Here we present the structure of a neuronal model description language, NeuroML. This provides a way to express these complex models in a common format based on the underlying physiology, allowing them to be mapped to multiple applications. We have tested this language by converting published neuronal models to NeuroML format and comparing their behavior on a number of commonly used simulators. Creating a common, accessible model description format will expose more of the model details to the wider neuroscience community, thus increasing their quality and reliability, as for other Open Source software. NeuroML will also allow a greater “ecosystem” of tools to be developed for building, simulating and analyzing these complex neuronal systems.
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神经结构：在3D空间中建模神经元网络的工具。
影响因子：
16.2
作者：
Gleeson P;Steuber V;Silver RA
通讯作者：
Silver RA
影响因子：
3
作者：
Gardner, Daniel;Goldberg, David H.;Grafstein, Bernice;Robert, Adrian;Gardner, Esther P.
通讯作者：
Gardner, Esther P.
影响因子：
3
作者：
Cannon, Robert C.;Gewaltig, Marc-Oliver;De Schutter, Erik
通讯作者：
De Schutter, Erik
影响因子：
3.5
作者：
Eppler, Jochen Martin;Helias, Moritz;Muller, Eilif;Diesmann, Markus;Gewaltig, Marc-Oliver
通讯作者：
Gewaltig, Marc-Oliver
影响因子：
2.5
作者：
Davison, AP;Feng, JF;Brown, D
通讯作者：
Brown, D