Data-Science Core

项目摘要 本申请建议使用理论驱动的实验设计，并采用先进的神经技术 记录、数据分析和计算建模，以研究神经机制、电路和 空间和时间中因果推理的知觉过程背后的表征。多学科的 拟议工作的性质需要财团成员之间的密切合作。数据科学的核心 将促进这种合作，并提供必要的工具来处理和分析大规模神经 在拟议的实验中收集的数据。为了实现这些目标，数据科学核心将依赖于 现有的基础设施、开放标准和尽可能多的开放源码软件。目标1将建立一个 统一数据标准以及数据交换和存储基础设施，使用由 国际脑实验室，它将元数据存储在一个关系的、可搜索的数据库中，并且是实验性的 并在单独的文件服务器上处理数据。GitHub将支持联合开发、交流和 数据预处理、处理和分析的基础代码的文档。要将数据与模型相关联， 通过实验记录的电压必须转换为标准化的尖峰时间和计数，而不需要 手工艺品或混乱物。目标2将为此开发一个原则性的、透明的和可重复的管道 对项目B和C中产生的所有神经生理学数据进行预处理，并将其应用于第一阶段 消除电和行为伪影，并将电压转换为尖峰时间和局部磁场电位。这个 第二阶段将使用神经活动的统计模型来识别和标记潜在的离群值。这条管道 将以标准化格式生成带注释和清理的数据，可用于执行可靠的 分析、模型拟合和假设检验。目标3将结合尖端方法并将它们转换为 可以可靠地应用于新数据的软件工具。这一努力的大部分将应用于潜伏期- 状态发现技术，共同适应刺激、模型驱动的假设潜在状态和 未观察到的潜伏状态，如缓慢波动。这一目标的中心工作是实施这些工具， 帮助团队将它们应用于协作生成的数据，并对其进行提炼以供公众使用。目标4是 通过上传数据的相关部分，与更广泛的研究社区共享实验数据 到公共的和可免费访问的存储库。代码、文档和用例将在 GitHub。使用标准数据结构、开放标准和开放源码软件将确保无障碍 神经科学研究人员的可获得性、易用性和重复性。在全职数据科学家的帮助下 受聘管理这些工作的数据科学核心将建立在已建立的数据存储和分析基础上 标准和方法，以产生清洁和标准化的数据，我们的财团可以用来关闭 在理论和实验之间循环。通过与其他研究人员共享代码、用例和数据， Project还将改进和扩展这些资源，以供其他人将来使用。

Project Summary This application proposes to use theory-driven experimental design, with advanced techniques for neural recording, data analysis, and computational modeling, to investigate the neural mechanisms, circuits, and representations underlying the perceptual process of causal inference in space and time. The multidisciplinary nature of the proposed work requires close collaboration among consortium members. The Data Science Core will facilitate this collaboration and provide the tools necessary to handle and analyze the large-scale neural data collected in the proposed experiments. To achieve these goals, the Data Science Core will rely on existing infrastructure, open standards, and open-source software as much as possible. Aim 1 will establish a unified data standard, and data exchange and storage infrastructure, using the architecture established by the International Brain Laboratory, which stores metadata in a relational, searchable database, and experimental and processed data on a separate file server. Github will enable joint development, exchange, and documentation of the code underlying data preprocessing, processing, and analysis. To relate data to models, voltages recorded experimentally must be transformed into standardized spike times and counts, without artifacts or confounds. Aim 2 will develop a principled, transparent, and reproducible pipeline for this preprocessing and apply it to all neurophysiological data generated in Projects B and C. The first stage will eliminate electrical and behavioral artifacts and convert voltages into spike times and local field potentials. The second stage will use a statistical model of neural activity to identify and label potential outliers. This pipeline will produce annotated and cleaned data in a standardized format that can be used to perform reliable analyses, model fitting, and hypothesis tests. Aim 3 will combine cutting-edge methods and convert them to software tools that can be reliably applied to new data. Most of this effort will be applied to variants of latent- state discovery techniques that jointly fit the influence of stimuli, model-driven hypothesized latent states, and unobserved latent states such as slow fluctuations. The central work of this aim is to implement those tools, help the team apply them to the data generated by the collaboration, and refine them for public use. Aim 4 is to share the experimental data with the wider research community by uploading the relevant portions of the data to public and freely accessible repositories. Code, documentation, and use cases will be made public on Github. The use of standard data structures, open standards, and open-source software will ensure barrier-free access, ease of use, and reproducibility for neuroscience researchers. With the help of a full-time data scientist hired to manage these efforts, the Data Science Core will build on established data storage and analysis standards and methods to produce cleaned and standardized data that our consortium can use to close the loop between theory and experiments. By sharing code, use cases, and data with other researchers, this project will also improve and extend these resources for future use by others.