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: 10306896
• 负责人: ANTON ARKHIPOV
• 金额: $ 66.24万
• 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/10306896
• 关键词: Address; Area; BRAIN initiative; Behavior; Biological; Biophysics; Brain; Brain region; Cells; Code; Communities; Complex; Computer Workstations; Computer software; Data; Data Analyses; Databases; Documentation; Educational workshop; European; FAIR principles; Feedback; Goals; Graph; Human; Illinois; Institutes; 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; Structural Models; Training; Universities; Vision; Visual; Visualization; Visualization software; Work; Zaleplon; 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; 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奏鸣曲数据格式推进生物现实的建模 大脑倡议的主要目标之一是将复合物，多模式数据提炼为预测框架 通过理论/建模。正如规划文件“大脑2025：科学愿景”敦促“理论与建模” 应编织成正在进行的实验的成功阶段，使桥梁能够从单个单元 对于连通性，人口动态和行为。 练习，部分是因为该领域需要支持这种复杂建模和模型标准的软件 共享和可重复性。 艾伦研究所（Allen Institute）开发了两个有力的工具来满足这些需求。一个是大脑建模 Toolkit（BMTK） - 以多个分辨率的模型构建和模拟的软件套件，从 生物物理详细的神经元模型的网络，点神经网络，覆盖人口统计数据 方法。另一个是奏鸣曲（可扩展的开放网络体系结构模板）数据格式， 它提供了用于存储和交换数据描述所有阶段的数据的计算有效解决方案 建模工作流程（例如，模型网络的结构，模拟的配置，模拟输出）。这些 工具是与许多计划协调开发的，例如神经元，巢，神经舞 边界，Neuroml，Pynn，Netpyne和人类脑项目。结果，BMTK和Sonata启用 许多应用程序并引起了重大兴趣，许多用户已经使用了这些工具。 最近，BMTK和Sonata在整合Allen Institute和 从文献到迄今为止大脑区域的一些最复杂，最现实的模型。 我们建议为BMTK和Sonata和 帮助将这些工具集成到社区中的模型构建和仿真实践中。此外，艾伦 Institute团队与伊利诺伊大学的团队联手，该团队开发了广泛使用的分子可视化 软件VMD。通过将此软件与奏鸣曲集成，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。这些工具在一起将有助于自由 模型的交换和可重复性以及支持复杂的建模工作 - 尤其是在 大规模的生物学现实模型依赖于实验数据的系统整合 - 用于新颖和 专家用户都一样。这些贡献将推动大脑倡议的优先领域的理论和数据领域 分析和综合方法，并将强烈支持公平性（可访问性，可访问性， 神经科学建模中的互操作性和数字资产的重用。