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展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 (e.g.视频）。这些数据集已经解锁了广泛的真正的变革性 科学的机会，因为它们使我们能够得出可靠的统计推断，对个人 动物实验对象在精确的封装时刻的时间。然而，这些统计模型 在计算机软件中实现是复杂的和非平凡的。在过去的十年里， 最初新生的神经数据科学和统计学子领域迅速发展，产生了一个 广泛的建模方法和大量的，破碎的景观“一次性” 支持单一统计建模方法的软件包。这种探索 多样化的统计方法一直是并将继续是 推进领域。尽管如此，将现有模式整合为 统一和可靠的软件包早就应该出现了。此外，这一努力必须解决 神经和行为数据呈指数级增长， 建模工作流。为了满足这些需求，该应用程序将开发新的软件 在神经科学中实现一组经过时间考验的统计模型。到 为了适应呈指数级增长的数据大小，该软件将基于最近 用于大规模机器学习的创新基础设施，包括灵活的 指定GPU加速计算和云计算框架， 在多台机器上并行地建立模型参数。最后，我们将制定程序， 神经科学实验室与格式化的原始数据集一起共享可重现的分析工作流程 通过BRAIN Initiative标准，包括一个用于构建URL共享的新框架， 在任何Web浏览器中运行的交互式数据可视化。总之，这些新 软件工具将加速神经科学的发现，使实验室能够快速地 并以透明和可复制的方式分享。