Unité de Neurosciences, Information et Complexité, CNRS UPR 3293 Gif-sur-Yvette, France.
Front Neuroinform. 2013 Dec 10;7:34. doi: 10.3389/fninf.2013.00034. eCollection 2013.
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.
计算资源的可用性不断提高，使得计算神经科学能够进行更详细、更真实的建模，从而转向更异构的神经元电路模型，并采用复杂的实验方案。这对现有的工具链提出了挑战，因为参与典型建模者工作流程的工具集也在同时扩展，它们之间流动的元数据的复杂性也在增加。对于工作流程的许多部分，都有一系列的工具可用；然而，许多领域缺乏专用工具，而现有工具的集成也受到限制。这迫使建模者要么手动处理工作流程，导致错误，要么编写大量代码来自动化工作流程的部分内容，在这两种情况下都会降低他们的工作效率。为了解决这些问题，我们开发了 Mozaik：一个用 Python 编写的用于尖峰神经元网络模拟的工作流程系统。Mozaik 将模型、实验和刺激规范、模拟执行、数据存储、数据分析和可视化集成到一个单一的自动化工作流程中，确保所有相关的元数据都可供所有工作流程组件使用。它基于几个现有的工具，包括 PyNN、Neo 和 Matplotlib。它提供了一种声明式的方法来使用分层组织的配置文件指定模型和记录配置。Mozaik 自动记录所有数据以及实验上下文的所有相关元数据，允许对分析和可视化阶段进行自动化。Mozaik 具有模块化的架构，现有的模块被设计为可以通过最小的编程工作进行扩展。Mozaik 通过自动化整个实验周期，提高了在高度结构化的神经元网络上运行虚拟实验的效率，同时通过减轻用户在手动处理各个工作流程阶段之间的元数据流方面的负担，提高了建模研究的可靠性。
