Unified, Scalable, and Reproducible Neurostatistical Software

统一、可扩展且可重复的神经统计软件

• 批准号: 10725500
• 负责人: Scott Warren Linderman
• 金额: $ 218.69万
• 依托单位: NEW YORK UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-10 至 2026-07-09
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10725500
• 关键词: Academy; Acceleration; Address; Adopted; Algorithms; Animal Behavior; Animals; BRAIN initiative; Behavioral; Categories; Cells; Cloud Computing; Code; Collaborations; Collection; Communities; Complex; Computer software; Custom; Data; Data Analyses; Data Analytics; Data Science; Data Set; Dimensions; Ecosystem; Electrophysiology (science); Encapsulated; Engineering; Fracture; Funding; Future; Goals; Hour; Individual; Infrastructure; Ingestion; Internet; Internships; Laboratories; Libraries; Literature; Machine Learning; Mathematics; Measurement; Mental disorders; Methodology; Methods; Modality; Modeling; Modernization; Neurons; Neurosciences; Population; Procedures; Process; Productivity; Proliferating; Publications; Pythons; Reproducibility; Research; Research Personnel; Resolution; Resources; Sampling; Scientific Advances and Accomplishments; Series; Services; Software Tools; Sorting; Specific qualifier value; Speed; Standardization; Statistical Methods; Statistical Models; Stereotyping; Students; Techniques; Testing; Time; Validation; Visualization; Visualization software; career development; data fusion; data modeling; data tools; data visualization; experimental study; feature extraction; flexibility; high dimensionality; high end computer; innovation; learning community; mathematical analysis; mathematical model; nervous system disorder; neural; neural circuit; novel; open source; parallelization; programs; software development; statistics

Project Summary Many advances in modern neuroscience rely on electrophysiological recordings of large neural populations (e.g. many hundreds of cells) or high-resolution measurements of animal behavior (e.g. from video). These datasets have unlocked a wide range of genuinely transformational scientific opportunities, as they enable us to draw reliable statistical inferences about individual animal subjects at precisely encapsulated moments in time. However, these statistical models are complex and non-trivial to implement in computer software. Over the past decade, an initially nascent sub-field of neural data science and statistics grew precipitously, producing a broad array of modeling approaches and a voluminous, fractured landscape of “one-off” software packages that support a single statistical modeling approach. This exploration of diverse statistical methodologies has been, and will continue to be, a crucial component to advancing the field. Nevertheless, a concerted effort to consolidate existing models into a unified and reliable software package is long overdue. Moreover, this effort must address the exponentially growing scale of neural and behavioral data, as well as the escalating intricacy of modeling workflows. To address these needs, this application will develop novel software implementations of a curated set of time-tested statistical models in neuroscience. To accommodate the exponentially growing data sizes, this software will be built on top of recently innovated infrastructure for large-scale machine learning, including flexible procedures for specifying GPU-accelerated computations and cloud computing frameworks to sweep across model parameters in parallel across many machines. Finally, we will develop procedures for neuroscience labs to share reproducible analysis workflows alongside raw datasets formatted by BRAIN Initiative standards, including a novel framework for building URL-shareable, interactive data visualizations that operate within any web browser. Altogether, these new software tools will accelerate neuroscience discoveries by enabling laboratories to rapidly iterate on in-house analyses and share them in a manner that is transparent and reproducible.

项目摘要 现代神经科学的许多进步都取决于大型神经科学的电生理记录 种群（例如数百个细胞）或动物行为的高分辨率测量 （例如，来自视频）。这些数据集解锁了各种真正的转型 科学机会，因为它们使我们能够对个人提取可靠的统计推断 动物受试者精确地封装了时间。但是，这些统计模型 在计算机软件中实现很复杂且不平淡。在过去的十年中， 最初，神经元数据科学和统计数据的新生子场精确地增长，产生 广泛的建模方法和大量的“一次性”景观 支持单个统计建模方法的软件包。这种探索 潜水员的统计方法已经并且将继续是 推进领域。然而，一致努力将现有模型巩固到一个 统一和可靠的软件包早就应该了。而且，这项工作必须解决 神经和行为数据的指数增长，以及不断升级的复杂性 建模工作流程。为了满足这些需求，该应用程序将开发新颖的软件 神经科学中一组经过时间考验的统计模型的实现。到 适应成倍增长的数据尺寸，该软件将建立在最近之上 用于大规模机器学习的创新基础设施，包括灵活的程序 指定GPU加速计算和云计算框架以扫描 在许多机器上并行的模型参数。最后，我们将制定程序 神经科学实验室与原始数据集共享可重现的分析工作流程 按大脑倡议标准，包括一个新颖的构建URL共享框架， 在任何Web浏览器中运行的交互式数据可视化。这些新的 软件工具将通过使实验室快速迭代来加速神经科学发现 在内部分析中，并以透明和繁殖的方式共享它们。