The Short Course on the Application of Machine Learning for Automated Quantification of Behavior

机器学习在行为自动量化中的应用短期课程

• 批准号: 10420570
• 负责人: Ishmail John Abdus-Saboor
• 金额: $ 15.46万
• 依托单位: JACKSON LABORATORY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10420570
• 关键词: Adoption; Algorithms; Animal Behavior; Animals; Automobile Driving; Awareness; Behavior; Behavioral; Behavioral Genetics; Biological; Biomedical Research; Collaborations; Complex; Computer Analysis; Computer Vision Systems; Computer software; Data; Data Analyses; Data Science; Data Set; Databases; Development; Disadvantaged; Disease; Education; Educational process of instructing; Educational workshop; Environment; Ethology; Experimental Designs; Explosion; Faculty; Fellowship; Fostering; Future Teacher; Generations; Genetic; Genetic Research; Genomics; Goals; Health; Human; Institution; Knowledge; Learning; Machine Learning; Measurement; Mentors; Methodology; Methods; Minority-Serving Institution; Modeling; Neurophysiology - biologic function; Neurosciences; Neurosciences Research; Participant; Performance; Persons; Positioning Attribute; Postdoctoral Fellow; Psychiatry; Public Health; Reproducibility; Research; Research Personnel; Resolution; Resources; Running; Schedule; Science; Scientist; Statistical Methods; Structure; Students; Supervision; Technology; The Jackson Laboratory; Time; Trainers Training; Training; Underrepresented Minority; Work; base; career; career development; computer science; data integration; data modeling; data streams; deep learning; design; experience; experimental study; gender minority; graduate student; improved; innovation; insight; learned behavior; learning materials; lectures; machine learning method; neural network; neuroethology; next generation; novel; programs; recruit; relating to nervous system; statistical learning; statistics; symposium; technology development; temporal measurement; tool; virtual

PROJECT SUMMARY/ABSTRACT Elucidating the mechanism and function of neural encodings and circuit dynamics has been a major challenge in neuroscience and behavioral analyses. However, quantitative behavior analysis has dramatically accelerated and improved with the implementation and application of new machine learning methods, including new deep learning-based methods to track animals at high temporal and spatial resolution. This technology has broad current and potential application that will impact a breadth of fields that have direct relevance and impact on studies of human health and disease, including the fields of neuroscience, behavior, genetics, psychiatry, and biomedicine. However, several roadblocks limit the widespread adoption of these tools and analyses. First, many tracking and behavior analysis packages require a high level of computational expertise and are thus limited in application to expert labs. Second, with high-resolution data streams, quantitating behavior requires new statistical tools and proper modeling of data. Since the application of machine learning to behavioral analyses is an emerging and key methodology, we recognize an unmet need for investigators in a variety of relevant fields to learn the fundamentals of its rigorous use. Thus, to train a new generation of interdisciplinary researchers at the interface of neuroscience, machine learning, and behavior, we propose to establish an annual 4-day workshop that brings together experts in quantitative behavior, computer vision, and experimental design to provide a practical introduction to the field of quantitative neuroethology and behavior: we propose the unique and timely interdisciplinary course The Short Course on the Application of Machine Learning for Automated Quantification of Behavior at the Jackson Laboratory (JAX). This Short Course will provide attendees (in-person and virtually) with; information on the state-of-the-art of machine learning based behavior quantitation, the fundamentals of behavior quantitation, hands-on workshops and data analysis, a forum for student-teacher interaction for networking, and training at the leading edge of computational ethology. Students will emerge from the course with the ability to: 1) design a high quality, adequately powered behavior experiment; 2) select and install a suitable platform for high-resolution analysis of animal behavior; 3) deploy a behavior data analysis strategy, including collecting new training datasets, training analysis software, and validating performance on held-out data; and 4) run workflows/pipelines that are necessary to analyze their data following extraction. To achieve this, we propose: Aim 1. To develop and deliver a 4-day workshop to train scientists on application of machine learning to animal behavior quantitation. Aim 2. To create an environment that will expand the field of quantitative behavior analysis by fostering idea generation, discussion, and collaboration to yield new discoveries, broader applications, and advance technology development. Aim 3. Foster the recruitment and development of diverse junior investigators in neuroscience, behavioral genetics, and quantitative analysis of animal behavior.

项目摘要/摘要 阐明神经编码和电路动力学的机制和功能一直是一个主要的挑战 在神经科学和行为分析方面。然而，量化行为分析已经大大加快了速度 并随着新的机器学习方法的实施和应用而得到改进，包括新的深度 基于学习的方法，以高时间和空间分辨率跟踪动物。这项技术具有广泛的应用前景 当前和潜在的应用将影响与以下方面直接相关和影响的领域的广度 对人类健康和疾病的研究，包括神经科学、行为学、遗传学、精神病学和 生物医学。然而，有几个障碍限制了这些工具和分析的广泛采用。首先，很多人 跟踪和行为分析包需要高水平的计算专业知识，因此受限于 应用于专家实验室。其次，对于高分辨率数据流，量化行为需要新的 统计工具和适当的数据建模。由于机器学习在行为分析中的应用 作为一种新兴和关键的方法，我们认识到在各种相关领域对调查人员的需求尚未得到满足 学习严格使用它的基本原理。因此，要培养新一代跨学科研究人员， 神经科学、机器学习和行为学的接口，我们建议建立每年4天的 汇聚了数量行为学、计算机视觉和实验设计专家的研讨会 为了提供量化神经行为学和行为学领域的实际介绍：我们建议 独特而及时的跨学科课程--机器学习在 杰克逊实验室(JAX)的行为自动量化。这一短期课程将为与会者提供 (面对面和虚拟地)；关于基于机器学习的行为量化的最新信息， 行为量化基础、实践工作坊和数据分析，为学生和教师提供的论坛 网络互动，以及在计算行为学前沿的培训。学生们将从 具备以下能力的课程：1)设计高质量、动力充足的行为实验；2)选择和 安装适合动物行为的高分辨率分析平台；3)部署行为数据分析 战略，包括收集新的培训数据集、培训分析软件和验证 保留的数据；以及4)运行在提取之后分析其数据所必需的工作流/管道。至 为了实现这一目标，我们建议：目标1.开发和提供一个为期4天的研讨会，培训科学家应用 机器学习对动物行为进行量化。目标2.创造一种环境，扩大 通过促进想法的生成、讨论和协作来进行量化行为分析，以产生新的 新发现、更广泛的应用和先进的技术发展。目标3.促进招聘和 在神经科学、行为遗传学和定量分析方面培养不同的初级研究人员 动物的行为。