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

传播数据驱动的脑回路多尺度建模工具

• 批准号: 10669218
• 负责人: Salvador Dura-Bernal
• 金额: $ 23.18万
• 依托单位: SUNY DOWNSTATE MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-18 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10669218
• 关键词: Advocate; Algorithms; Area; BRAIN initiative; Behavior; Biophysics; Brain; Brain region; Cells; Code; Communities; Complement; Complex; Computer Models; Computer Simulation; Computers; Data; Data Analyses; Data Set; Dendrites; Diffusion; Disparate; Documentation; Dura Mater; Education; Educational process of instructing; Educational workshop; Electroencephalography; Electrophysiology (science); Ensure; Frustration; Generations; Growth; Human; Image; Institution; Instruction; International; Internet; Intuition; Ion Channel; Language; Libraries; Location; Measures; Methods; Modeling; Modernization; Molecular; Multimedia; Neocortex; Neurons; Neurophysiology - biologic function; Neurosciences; Neurosciences Research; Newsletter; Online Systems; Operating System; Pattern; Peer Review; Performance; Persons; Population; Publications; Pythons; Quality Control; Reaction; Reporting; Reproducibility; Research; Research Personnel; Resources; Running; Second Messenger Systems; Signal Transduction; Software Tools; Solid; Specific qualifier value; Standardization; Structure; Students; Synapses; System; Testing; Time; Training; Translating; Update; Validation; Visualization; Work; cell type; cloud platform; computational neuroscience; computerized tools; coping; data integration; data tools; data visualization; design; dissemination strategy; experimental study; flexibility; graphical user interface; improved; innovative neurotechnologies; insight; large datasets; model building; model design; multi-scale modeling; neglect; neocortical; network models; neural; neural model; novel; online community; online tutorial; open source; prevent; prototype; rapid growth; simulation; student training; supercomputer; symposium; theories; three-dimensional visualization; tool; tool development; user-friendly

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.

总结 标题：传播用于大脑电路数据驱动多尺度建模的工具。 PI：S Dura-Bernal 我们正在开发一种新的软件工具，称为NetPyNE，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艾德计算模型中。然后用户可以模拟和分析这一点 模型，以更好地了解大脑的结构，动力学和功能在一个独特的框架，结合： 1.使用灵活、基于规则的高级标准化规范进行编程或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， 模型易于阅读、修改、共享和重用; 3.支持从分子到细胞再到网络的多尺度; 4.支持复杂的亚细胞机制、树突连接和刺激模式; 5.有效平行 在独立计算机和超级计算机上进行模拟; 6.自动数据分析和可视化（例如， 连接，神经活动，信息理论分析）; 7.从多个标准化的 格式; 8.使用网格搜索和进化算法进行自动参数调整（分子到网络水平）。 NetPyNE的潜力，以贝内研究界是证明了几个同行评审的出版物， 用户和拥护者的稳步增长。我们实验室和合作者实验室的50多名研究人员和学生 将该工具的原型用于教育或研究各种大脑区域和现象。有一个 活跃的在线社区，他们合作为项目做出贡献，通过 GitHub平台，一个邮件列表和两个问答论坛。计算神经科学组织（Organization for Computational Neuroscience） 2-关于NetPyNE的2019年冬季通讯的页面专题文章。NetPyNE也正在与其他 神经科学社区的资源：人类新皮质神经求解器，开源大脑，神经科学 Gateway以及NeuroML和SONATA国际标准化网络格式。 我们的建议旨在将NetPyNE转变为一个功能齐全的GUI，并广泛使用的可靠且经过良好测试的工具。 在科学界传播该工具。该工具的快速增长意味着许多功能已经 以有限的资源和时间快速增加。我们现在将确保所有这些功能都得到适当的评估， 可靠性、鲁棒性和可扩展性，有充分的文档记录并集成到GUI中。GUI也将扩展 提供基于网络的在线访问，并支持更大模型的可视化。我们还将开发互动 在线教程，以清楚地解释和演示我们的软件包中包含的丰富多样的功能。 通过每年一次的为期多天的课程和神经科学会议上的教程/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 建模多尺度建模通过结合和解释先前的实验， 不可复制的数据集。使用NetPyNE开发的模拟和分析提供了一种更好地理解 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 动力学，人口振荡，EEG/MEG信号，和信息理论的措施。