morphforge: a toolbox for simulating small networks of biologically detailed neurons in Python.
morphforge: a toolbox for simulating small networks of biologically detailed neurons in Python.
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DOI：
10.3389/fninf.2013.00047
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发表时间：
2013
影响因子：
3.5
通讯作者：
Willshaw DJ
中科院分区：
文献类型：
--
作者：
Hull MJ;Willshaw DJ
建模研究的大致结构通常可以在一杯咖啡的时间内得到解释，但将这种高水平的概念想法转换为最终模拟结果的图表可能需要数周的时间坐在电脑前。尽管模型本身可能很复杂，但许多智力资源经常被浪费在处理软件生态系统的复杂性上，例如努力管理文件、工具和数据格式之间的接口、查找代码中的错误或计算变量单位。Morforge是一个高级的、用于构建和管理小群体多隔室生物物理模型神经元的模拟的Python工具箱。完整的In Silico实验，包括神经元形态的定义、通道描述、刺激、可视化和结果分析，可以使用高级对象在单个简短的Python脚本中编写。可以从单个脚本创建和运行多个独立的模拟，从而允许调查参数空间。考虑了算法组件和可参数组件的重复使用，以允许特定和随机参数变化。该工具箱的一些其他功能包括：自动生成关于模拟的人类可读文档(例如，PDF文件)；透明地处理不同的生物物理单元；基于标签系统绘制模拟结果的新机制；以及既支持使用定义通道和突触的既定格式(例如，MODL文件)的体系结构，也支持轻松支持其他库和标准的可能性。我们希望这个工具箱将允许科学家快速建立用于研究的多室模型神经元的模拟，并作为进一步工具开发的平台。
The broad structure of a modeling study can often be explained over a cup of coffee, but converting this high-level conceptual idea into graphs of the final simulation results may require many weeks of sitting at a computer. Although models themselves can be complex, often many mental resources are wasted working around complexities of the software ecosystem such as fighting to manage files, interfacing between tools and data formats, finding mistakes in code or working out the units of variables. morphforge is a high-level, Python toolbox for building and managing simulations of small populations of multicompartmental biophysical model neurons. An entire in silico experiment, including the definition of neuronal morphologies, channel descriptions, stimuli, visualization and analysis of results can be written within a single short Python script using high-level objects. Multiple independent simulations can be created and run from a single script, allowing parameter spaces to be investigated. Consideration has been given to the reuse of both algorithmic and parameterizable components to allow both specific and stochastic parameter variations. Some other features of the toolbox include: the automatic generation of human-readable documentation (e.g., PDF files) about a simulation; the transparent handling of different biophysical units; a novel mechanism for plotting simulation results based on a system of tags; and an architecture that supports both the use of established formats for defining channels and synapses (e.g., MODL files), and the possibility to support other libraries and standards easily. We hope that this toolbox will allow scientists to quickly build simulations of multicompartmental model neurons for research and serve as a platform for further tool development.
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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：
4.3
作者：
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Laughlin, Simon B.
影响因子：
3.5
作者：
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通讯作者：
Gewaltig, Marc-Oliver
影响因子：
1.3
作者：
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通讯作者：
COOLEY, JW
影响因子：
1.2
作者：
Hines, ML;Morse, T;Shepherd, GM
通讯作者：
Shepherd, GM