Tools for Model Discovery, Validation and Selection in Neuroscience with NeuroML

使用 NeuroML 进行神经科学模型发现、验证和选择的工具

• 批准号: 9043657
• 负责人: Sharon Marie Crook
• 金额: $ 37.73万
• 依托单位: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-25 至 2019-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9043657
• 关键词: Address; Aging; Biological; Brain; Caenorhabditis elegans; Code; Communities; Computer Simulation; Computer software; Data; Data Reporting; Databases; Development; Disease; Documentation; Educational workshop; Environment; Feedback; Generations; Goals; Health; Imagery; International; Internet; Ion Channel; Lead; Literature; Modeling; Movement; Nervous System Physiology; Nervous system structure; Neurons; Neurosciences; Neurosciences Research; Pathology; Process; Property; Protocols documentation; Publishing; Reproducibility; Research Infrastructure; Research Personnel; Resources; Science; Scientist; Software Tools; System; Testing; Trauma; Validation; Work; base; computational neuroscience; flexibility; model development; multi-scale modeling; network models; neural circuit; neurophysiology; olfactory bulb; open source; outreach; relating to nervous system; repository; research study; simulation; tool; uptake; user-friendly

 DESCRIPTION (provided by applicant): Building computational models is one of the best ways of furthering our understanding of both the normal function of the nervous system and the pathology associated with aging, neural trauma, or disease. Biophysically detailed models provide a framework for integrating data across spatial scales and for exploring hypotheses about the biological mechanisms underlying neuronal and network dynamics. However, as models increase in complexity, additional barriers emerge to the creation, validation, exchange and re-use of models. The NeuroML project aims to address these issues by providing a standard format for describing multiscale models in neuroscience. NeuroML is supported by over 30 tools and databases and is the basis for model exchange at Open Source Brain, where 374 users are collaborating on 57 public modeling projects. In spite of this promising movement toward model sharing in the neuroscience community, it is extremely rare to see a specific, rigorous statement of the criteria used for evaluating models during model development, and multiple models for the same ion channels and neurons are not compared for concordance with the same suite of experimental data. The overall goal of this project is to create a flexible infrastructure for assessing the scope and quality of computational models in neuroscience and to make this information broadly available to the community for a large class of models. Aim 1 focuses on enhancing existing tools to work together seamlessly for validation of NeuroML models against experimental data. Aim 2 concentrates on the development of a dedicated web portal, incorporation of automated model validation into existing model sharing platforms, and the creation of documentation, tutorials, forums and other outreach for promoting uptake and obtaining user feedback. Aim 3 includes testing of the validation tool chain in multiple large-scale neuronal network modeling environments. The proposed activities will build bridges that connect multiple, existing initiatives in support of model development, validation, exchange, selection, and re-use, and will integrate experimental data with modeling efforts for more efficiency, better transparency, and greater impact of computational models in neuroscience research.

 描述（由适用提供）：建立计算模型是进一步了解神经系统正常功能以及与衰老，神经元创伤或疾病相关的病理学的最佳方法之一。生物物理详细的模型提供了一个框架，用于整合跨空间尺度的数据，并探索有关神经元和网络动力学基础生物机制的假设。但是，随着模型的复杂性的增加，模型的创建，验证，交换和重复使用的创建，验证，交换和重新使用。 Neuroml项目旨在通过提供描述神经科学中多尺度模型的标准格式来解决这些问题。 Neuroml得到了30多个工具和数据库的支持，并且是开源大脑模型交换的基础，其中374位用户正在57个公共建模项目上合作。尽管这种有望在神经科学界进行模型共享的承诺运动，但很少见到用于评估模型开发过程中用于评估模型的标准的特定，严格的陈述，并且没有比较相同的离子通道和神经元的多个模型，以与相同的实验数据套件进行比较。该项目的总体目标是创建一个灵活的基础架构，以评估神经科学中计算模型的范围和质量，并使社区广泛地用于大量模型。 AIM 1专注于增强现有的工具，以无缝地共同努力，以验证针对实验数据的神经模型。 AIM 2专注于开发专用的Web门户网站，将自动化模型验证纳入现有模型共享平台，以及创建文档，教程，论坛和其他外展，以促进吸收和获得用户反馈。 AIM 3包括在多个大型神经网络建模环境中测试验证工具链。拟议的活动将建立桥梁，以支持模型开发，验证，交换，选择和重复使用的多个现有计划，并将实验数据与建模工作集成，以提高效率，更好的透明度和计算模型对神经科学研究的影响。