Advancing Bio-Realistic Modeling via the Brain Modeling ToolKit and SONATA Data Format

通过大脑建模工具包和 SONATA 数据格式推进生物真实建模

• 批准号: 10477439
• 负责人: ANTON ARKHIPOV
• 金额: $ 74.79万
• 依托单位: ALLEN INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10477439
• 关键词: Address; Area; BRAIN initiative; Behavior; Biophysics; Brain; Brain region; Cells; Code; Communities; Complex; Computer software; Data; Data Analyses; Databases; Dedications; Documentation; Educational workshop; European; FAIR principles; Feedback; Goals; Graph; Human; Illinois; Intuition; Language; Literature; Manuscripts; Modality; Modeling; Molecular; Neurons; Neurosciences; Output; Personal Computers; Persons; Population Dynamics; Population Statistics; Property; Publications; Reproducibility; Resolution; Resources; Running; Software Tools; Structure; Training; Universities; Vision; Visual; Visualization; Visualization software; Work; base; data exchange; data format; data integration; digital; diverse data; experimental study; file format; flexibility; graphical user interface; interest; model building; models and simulation; movie; multimodal data; network architecture; network models; public database; response; scripting interface; simulation; theories; tool; user-friendly

Advancing Bio-Realistic Modeling via the Brain Modeling ToolKit and SONATA Data Format One of the major goals of the BRAIN Initiative is to distill complex, multi-modal data into predictive frameworks via theory/modeling. As the planning document "BRAIN 2025: A Scientific Vision" urges, “theory and modeling should be woven into successive stages of ongoing experiments, enabling bridges to be built from single cells to connectivity, population dynamics, and behavior.” However, data-driven, bio-realistic modeling is not widely practiced, in part because the field needs software supporting such complex modeling and standards for model sharing and reproducibility. The Allen Institute has developed two powerful tools addressing these needs. One is the Brain Modeling ToolKit (BMTK) – a software suite for model building and simulation at multiple levels of resolution, from networks of biophysically detailed neuronal models, to point-neuron networks, to population-statistics approaches. The other one is the SONATA (Scalable Open Network Architecture TemplAte) data format, which provides computationally efficient solutions for storing and exchanging data describing all stages of the modeling workflow (e.g., structure of model networks, configuration of simulations, simulation outputs). These tools were developed in coordination with many initiatives, such as NEURON, NEST, Neurodata Without Borders, NeuroML, PyNN, NetPyNE, and the Human Brain Project. As a result, BMTK and SONATA enable many applications and have generated substantial interest, with many users already employing these tools. Most recently, BMTK and SONATA were instrumental in integrating diverse data from the Allen Institute and from the literature into some of the most sophisticated and bio-realistic models of a brain region to date. We propose to build a comprehensive user support and dissemination platform for BMTK and SONATA and help integrate these tools into model building and simulation practices in the community. In addition, the Allen Institute team joins forces with a University of Illinois team that developed a widely used molecular visualization software VMD. By integrating this software with SONATA, we will leverage its powerful existing capabilities to offer a free, highly efficient visualization tool for neuroscience modeling. Together, these tools will facilitate free exchange and reproducibility of models and support sophisticated modeling work – especially in cases of large-scale biologically realistic models relying on systematic integration of experimental data – for novice and expert users alike. These contributions will advance the BRAIN Initiative’s priority areas of Theory and Data Analysis and Integrated Approaches and will strongly facilitate FAIRness (Findability, Accessibility, Interoperability, and Reuse of digital assets) in neuroscience modeling.

通过大脑建模工具包和SONATA数据格式推进生物现实建模 BRAIN计划的主要目标之一是将复杂的多模态数据提取到预测框架中 通过理论/建模。正如规划文件《大脑2025：科学愿景》所敦促的那样，“理论和建模 应该被编织到正在进行的实验的连续阶段中，使桥梁能够从单细胞中建立起来。 连接性、种群动态和行为。”然而，数据驱动的、生物逼真的建模并没有广泛地应用于 实践，部分原因是该领域需要支持这种复杂建模和模型标准的软件 共享和可复制性。 艾伦研究所开发了两个强大的工具来满足这些需求。一个是大脑建模 ToolKit（BMTK）-一个软件套件，用于在多个分辨率级别上进行模型构建和仿真， 从生物病理学上详细的神经元模型网络，到点神经元网络，再到人口统计学 接近。另一种是SONATA（可扩展开放网络架构模板）数据格式， 其提供用于存储和交换描述所述过程的所有阶段的数据的计算上有效的解决方案。 建模工作流（例如，模型网络的结构、模拟的配置、模拟输出）。这些 工具是与许多倡议协调开发的，如NEURON、NEST、Neurodata Without Borders、NeuroML、PyNN、NetPyNE和人脑计划。因此，BMTK和SONATA使 许多应用程序，并产生了很大的兴趣，许多用户已经使用这些工具。 最近，BMTK和SONATA在整合来自艾伦研究所的各种数据方面发挥了重要作用， 从文献到一些最复杂的和生物现实的大脑区域模型的日期。 我们建议为BMTK和SONATA建立一个全面的用户支持和传播平台， 帮助将这些工具集成到社区的模型构建和模拟实践中。此外，艾伦 研究所团队与伊利诺伊大学团队合作，开发了广泛使用的分子可视化 软件VMD。通过将该软件与SONATA集成，我们将利用其强大的现有功能， 为神经科学建模提供免费、高效的可视化工具。这些工具将共同促进免费 模型的交换和再现，并支持复杂的建模工作-特别是在 大规模的生物现实模型依赖于实验数据的系统整合-对于新手和 专家用户一样。这些贡献将推进BRAIN计划的理论和数据优先领域 分析和综合方法，并将大大促进公平性（可查找性，可访问性， 互操作性和数字资产的重用）在神经科学建模中。