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(申请人提供)：建立计算模型是加深我们对神经系统正常功能和与衰老、神经创伤或疾病相关的病理的理解的最好方法之一。详细的生物物理学模型提供了一个框架，用于整合跨空间尺度的数据，并探索关于神经元和网络动力学基础上的生物机制的假设。然而，随着模型复杂性的增加，在模型的创建、验证、交换和重用方面出现了更多的障碍。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，是Open Source Brain模型交换的基础，那里有374个用户在57个公共建模项目上进行合作。尽管神经科学界朝着模型共享的方向迈进了一大步，但在模型开发过程中用于评估模型的标准的具体、严格的声明是极其罕见的，而且同一离子通道和神经元的多个模型与同一组实验数据的一致性不被比较。这个项目的总体目标是创建一个灵活的基础设施，用于评估神经科学中计算模型的范围和质量，并将这些信息广泛地提供给社区，用于大类模型。目标1专注于增强现有工具，以无缝协作，根据实验数据验证NeuroML模型。AIM 2的重点是开发一个专门的门户网站，将自动模型验证纳入现有的模型共享平台，并创建文件、教程、论坛和其他外联活动，以促进理解和获得用户反馈。目标3包括在多个大规模神经元网络建模环境中测试验证工具链。拟议的活动将搭建桥梁，连接支持模型开发、验证、交换、选择和重复使用的多个现有倡议，并将实验数据与建模工作相结合，以提高计算模型在神经科学研究中的效率、透明度和更大影响。