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.

概括 标题：用于数据驱动的脑电路多尺度建模工具。 pi：s dura-bernal 我们正在开发一种名为Netpyne的新型软件工具，该工具使用户能够合并复杂的实验 来自不同尺度的数据到一个统一的计算模型。然后，用户能够模拟和分析 更好地理解大脑结构，动态和功能的模型，以结合的独特框架： 1。使用基于规则的高级标准化规范的程序化或GUI驱动的模型构建； 2。模型参数与潜在的技术实现，防止编码错误和制作的分离 模型易于阅读，修改，共享和重复使用； 3。支持从分子到细胞再到网络的多个量表； 4。支持复杂的亚细胞机制，树突连通性和刺激模式； 5。有效的平行 在独立计算机和超级计算机上进行仿真； 6。自动数据分析和可视化（例如， 连通性，神经活动，信息理论分析）； 7。向/来自多个标准化的进口和导出 格式； 8。使用网格搜索和进化算法的自动参数调整（分子至网络水平）。 Netpyne有利于研究社区的潜力，有几个经过同行评审的出版物以及 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有 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 积极的在线社区，为项目做出贡献，发布问题和请求功能 GitHub平台，邮件列表和两个问答论坛。计算神经科学组织包括 Netpyne在2019年冬季新闻通讯中有关Netpyne的两页专题文章。 Netpyne也正在与其他 神经科学社区的资源：人类新皮质神经覆盖，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转变为具有功能齐全的GUI的坚实且经过充分测试的工具，并且广泛 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存在 以有限的资源和时间添加了快速的速度。现在，我们将确保对所有这些功能进行正确评估 可靠性，鲁棒性和可扩展性，已有文献记载并纳入GUI。 GUI也将扩展 提供基于Web的在线访问和支持较大模型的可视化。我们还将发展互动 在线教程清楚地解释并演示了我们包装中包含的充足和潜水员的功能。 通过在神经科学会议上的年度多天课程和教程/讲习班，我们将参与和培训 学生，实验和计算神经科学家以及使用Netpyne进行多尺度神经元的临床医生 造型。多尺度建模完成实验，以前结合和制造可解释 不可超过的数据集。 Netpyne开发的模拟和分析提供了一种更好地了解的方法 整个脑尺度的相互作用，包括分子浓度，细胞生物物理学，电生理学，神经元 动力学，人群振荡，脑电图/MEG信号和信息理论测量。