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.

项目概要 现代神经科学的许多进步依赖于大神经元的电生理记录 群体（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）。这些数据集解锁了广泛的真正变革性的内容。 科学机会，因为它们使我们能够对个人得出可靠的统计推论 然而，这些统计模型在精确封装的时刻的动物受试者。 在计算机软件中实现起来是复杂且不简单的。 神经数据科学和统计学最初的新兴子领域迅速发展，产生了 广泛的建模方法和大量、支离破碎的“一次性”景观 支持单一统计建模方法的软件包。 多样化的统计方法已经并将继续是 尽管如此，仍需共同努力将现有模型整合为一个新的模型。 统一和可靠的软件包早就应该解决了。 神经和行为数据规模呈指数级增长，以及复杂性不断升级 为了满足这些需求，该应用程序将开发新颖的软件。 在神经科学领域实施一组经过时间检验的统计模型。 为了适应呈指数级增长的数据规模，该软件将构建在最近的基础上 大规模机器学习的创新基础设施，包括灵活的程序 指定要横扫的 GPU 加速计算和云计算框架 最后，我们将开发用于跨多台机器并行建模参数的程序。 神经科学实验室将共享可重复的分析工作流程以及格式化的原始数据集 通过 BRAIN Initiative 标准，包括用于构建可共享 URL 的新颖框架， 总而言之，这些新功能可以在任何网络浏览器中运行的交互式数据可视化。 软件工具将使实验室能够快速迭代，从而加速神经科学的发现 内部分析并以透明且可重复的方式分享。