Integrated workflows for spiking neuronal network simulations.
Integrated workflows for spiking neuronal network simulations.
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DOI：
10.3389/fninf.2013.00034
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发表时间：
2013
影响因子：
3.5
通讯作者：
Davison AP
中科院分区：
文献类型：
--
作者：
Antolík J;Davison AP
计算资源的日益可获得性使计算神经科学中更详细、更现实的建模成为可能，导致了对神经元电路的更多异质模型的转变，并采用了复杂的实验方案。这给现有的工具链带来了挑战，因为典型建模师的工作流程中涉及的工具集正在随之扩展，它们之间流动的元数据也变得越来越复杂。对于工作流程的许多部分，有一系列工具可用；然而，许多领域缺乏专用工具，而现有工具的集成也很有限。这迫使建模师要么手动处理工作流，导致错误，要么编写大量代码来自动化部分工作流，在这两种情况下都会降低他们的生产率。为了解决这些问题，我们开发了Mozaik：一个用Python语言编写的工作流系统，用于尖峰神经元网络模拟。Mozaik将模型、实验和模拟规范、模拟执行、数据存储、数据分析和可视化集成到单个自动化工作流中，确保所有相关元数据可用于所有工作流组件。它基于几个现有的工具，包括Pynn、Neo和Matplotlib。它提供了一种使用分层组织的配置文件来指定模型和记录配置的声明性方法。Mozaik自动记录所有数据以及有关实验环境的所有相关元数据，从而实现分析和可视化阶段的自动化。Mozaik采用模块化架构，现有模块设计为只需最少的编程工作即可扩展。Mozaik通过自动化整个实验周期，提高了在高度结构化的神经网络上运行虚拟实验的生产率，同时通过使用户不必手动处理各个工作流程阶段之间的元数据流动，提高了建模研究的可靠性。
The increasing availability of computational resources is enabling more detailed, realistic modeling in computational neuroscience, resulting in a shift toward more heterogeneous models of neuronal circuits, and employment of complex experimental protocols. This poses a challenge for existing tool chains, as the set of tools involved in a typical modeler's workflow is expanding concomitantly, with growing complexity in the metadata flowing between them. For many parts of the workflow, a range of tools is available; however, numerous areas lack dedicated tools, while integration of existing tools is limited. This forces modelers to either handle the workflow manually, leading to errors, or to write substantial amounts of code to automate parts of the workflow, in both cases reducing their productivity. To address these issues, we have developed Mozaik: a workflow system for spiking neuronal network simulations written in Python. Mozaik integrates model, experiment and stimulation specification, simulation execution, data storage, data analysis and visualization into a single automated workflow, ensuring that all relevant metadata are available to all workflow components. It is based on several existing tools, including PyNN, Neo, and Matplotlib. It offers a declarative way to specify models and recording configurations using hierarchically organized configuration files. Mozaik automatically records all data together with all relevant metadata about the experimental context, allowing automation of the analysis and visualization stages. Mozaik has a modular architecture, and the existing modules are designed to be extensible with minimal programming effort. Mozaik increases the productivity of running virtual experiments on highly structured neuronal networks by automating the entire experimental cycle, while increasing the reliability of modeling studies by relieving the user from manual handling of the flow of metadata between the individual workflow stages.
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影响因子：
5.3
作者：
Vogels, TP;Abbott, LF
通讯作者：
Abbott, LF
DOI：
10.1093/cercor/bhs358
发表时间：
2014-03
期刊：
Cerebral cortex (New York, N.Y. : 1991)
影响因子：
--
作者：
Potjans TC;Diesmann M
通讯作者：
Diesmann M
影响因子：
16.2
作者：
Reimann MW;Anastassiou CA;Perin R;Hill SL;Markram H;Koch C
通讯作者：
Koch C
影响因子：
3.5
作者：
Davison AP;Brüderle D;Eppler J;Kremkow J;Muller E;Pecevski D;Perrinet L;Yger P
通讯作者：
Yger P
影响因子：
5.7
作者：
Nakagawa, Tristan T.;Jirsa, Viktor K.;Deco, Gustavo
通讯作者：
Deco, Gustavo