Dissemination of a tool for data-driven multiscale modeling of brain circuits

总结 标题：传播数据驱动的脑回路多尺度建模工具。 PI：S杜拉-伯纳尔 我们正在开发一种新颖的软件工具，称为 NetPyNE，它使用户能够整合复杂的实验 将不同尺度的数据整合到统一的计算模型中。然后用户就能够模拟和分析这一点 模型可以在一个独特的框架中更好地理解大脑结构、动力学和功能，该框架结合了： 1. 使用灵活的、基于规则的、高级标准化规范进行程序化或 GUI 驱动的模型构建； 2. 将模型参数与底层技术实现分离，防止编码错误并使得 模型更易于阅读、修改、共享和重用； 3.支持从分子到细胞到网络的多个尺度； 4. 支持复杂的亚细胞机制、树突连接和刺激模式； 5. 高效并行 在独立计算机和超级计算机上进行模拟； 6. 自动化数据分析和可视化（例如， 连接性、神经活动、信息理论分析）； 7. 与多个标准化的导入和导出 格式； 8.使用网格搜索和进化算法进行自动参数调整（分子到网络级别）。 NetPyNE 使研究界受益的潜力已被多个同行评审出版物和 用户和拥护者的稳定增长。我们实验室和合作者实验室的 50 多名研究人员和学生已经 使用该工具的原型进行教育或研究各种大脑区域和现象。有一个 活跃的在线社区，他们通过以下方式协作为该项目做出贡献、发布问题和请求功能 GitHub 平台、一个邮件列表和两个问答论坛。计算神经科学组织包括 NetPyNE 2019 年冬季通讯中的 2 页专题文章。 NetPyNE 也正在与其他集成 神经科学界的资源：人类新皮质神经解算器、开源大脑、神经科学 网关，以及NeuroML和SONATA国际标准化网络格式。 我们的建议旨在将 NetPyNE 转变为一个可靠且经过良好测试的工具，具有功能齐全的 GUI，并广泛使用 在科学界传播该工具。该工具的快速发展意味着许多功能已经被 在资源和时间有限的情况下快速添加。我们现在将确保所有这些功能都得到正确评估 可靠性、稳健性和可扩展性，有详细记录并纳入 GUI 中。 GUI也将得到扩展 提供基于网络的在线访问并支持较大模型的可视化。我们还将开发互动 在线教程清楚地解释和演示了我们的包中包含的丰富多样的功能。 通过每年在神经科学会议上进行的多日课程和教程/研讨会，我们将参与和培训 学生、实验和计算神经科学家以及临床医生使用 NetPyNE 进行多尺度神经网络 建模。多尺度建模通过组合和解释先前的实验来补充实验 不可通约的数据集。使用 NetPyNE 开发的模拟和分析提供了一种更好地理解的方法 跨大脑尺度的相互作用，包括分子浓度、细胞生物物理学、电生理学、神经学 动力学、群体振荡、EEG/MEG 信号和信息论测量。

Summary Title: Dissemination of a tool for data-driven multiscale modeling of brain circuits. PI: S Dura-Bernal We are developing a novel software tool, called NetPyNE, that enables users to consolidate complex experimental data from different scales into a unified computational model. Users are then be able to simulate and analyze this model to better understand brain structure, dynamics and function in a unique framework that combines: 1. programmatic or GUI-driven model building using flexible, rule-based, high-level standardized specifications; 2. separation of model parameters from underlying technical implementations, preventing coding errors and making models easier to read, modify, share and reuse; 3. support for multiple scales from molecule to cell to network; 4. support for complex subcellular mechanisms, dendritic connectivity and stimulation patterns; 5. efficient parallel simulation both on stand-alone computers and supercomputers; 6. automated data analysis and visualization (e.g., connectivity, neural activity, information theoretic analysis); 7. importing and exporting to/from multiple standardized formats; 8. automated parameter tuning (molecule to network level) using grid search and evolutionary algorithms. NetPyNE's potential to benefit the research community is evidenced by several peer-reviewed publications and by the steady growth of users and advocates. Over 50 researchers and students in our lab and collaborators' labs have used a prototype of the tool for education or to investigate a variety of brain regions and phenomena. There is an active online community who collaboratively contribute to the project, post questions and request features via the GitHub platform, a mailing list and two Q&A forums. The Organization for Computational Neuroscience included a 2-page feature article on NetPyNE in their 2019 Winter Newsletter. NetPyNE is also being integrated with other resources in the neuroscience community: Human Neocortical Neurosolver, Open Source Brain, Neuroscience Gateway, and the NeuroML and SONATA international standardized network formats. Our proposal is aimed at transforming NetPyNE into a solid and well-tested tool with a fully-featured GUI, and widely disseminating the tool among the scientific community. The rapid growth of the tool means many features have been added at a fast pace, with limited resources and time. We will now ensure all these features are properly evaluated for reliability, robustness and scalability, well documented and incorporated into the GUI. The GUI will also be extended to provide online web-based access and support visualization of larger models. We will also develop interactive online tutorials to clearly explain and demonstrate the ample and diverse functionality included in our package. Through a yearly multi-day course and tutorials/workshops at neuroscience conferences we will engage and train students, experimental and computational neuroscientists, and clinicians in using NetPyNE for multiscale neural modeling. Multiscale modeling complements experimentation by combining and making interpretable previously incommensurable datasets. Simulations and analyses developed with NetPyNE provide a way to better understand interactions across the brain scales, including molecular concentrations, cell biophysics, electrophysiology, neural dynamics, population oscillations, EEG/MEG signals, and information theoretic measures.