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: 10600079
• 负责人: VIVEK KUMAR
• 金额: $ 15.44万
• 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/10600079
• 关键词: Acceleration; Adoption; Algorithms; Animal Behavior; Animals; Automobile Driving; Awareness; Behavior; Behavioral; Behavioral Genetics; Biological; Biomedical Research; Collaborations; Complex; Computer Analysis; Computer Vision Systems; Computer software; Creativeness; Data; Data Analyses; Data Science; Data Set; Databases; Democracy; 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; Laboratories; Learning; Machine Learning; Measurement; Mentors; Methodology; Methods; Minority-Serving Institution; Modeling; Neurophysiology - biologic function; Neurosciences; Neurosciences Research; Participant; Performance; Persons; Positioning Attribute; Postdoctoral Fellow; Productivity; Psychiatry; Public Health; Reproducibility; Research; Research Personnel; Resolution; Resources; Running; Schedule; Science; Scientist; Statistical Methods; Structure; Students; Technology; The Jackson Laboratory; Trainers Training; Training; Underrepresented Minority; Work; career; career development; complex data; computer science; data modeling; data streams; deep learning; design; experience; experimental study; gender minority; graduate student; improved; innovation; insight; learning materials; lectures; machine learning method; neural; neural network; neuroethology; next generation; novel; programs; recruit; statistical learning; statistics; symposium; synergism; 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.

项目总结/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重大挑战 在神经科学和行为分析方面。然而，定量行为分析已经大大加快了 并随着新机器学习方法的实施和应用而改进，包括新的深度学习方法。 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 当前和潜在的应用程序，将影响广泛的领域，有直接的相关性和影响， 人类健康和疾病的研究，包括神经科学，行为，遗传学，精神病学， 生物医药然而，一些障碍限制了这些工具和分析的广泛采用。一是很多 跟踪和行为分析软件包需要高水平的计算专业知识，因此在以下方面受到限制： 应用到专家实验室。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 作为一种新兴的关键方法，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，为了培养新一代的跨学科研究人员， 神经科学，机器学习和行为的接口，我们建议每年建立一个为期4天的 研讨会，汇集了定量行为，计算机视觉和实验设计方面的专家 为了给定量神经行为学和行为学领域提供一个实用的介绍：我们提出了 独特而及时的跨学科课程机器学习应用短期课程， 杰克逊实验室（JAX）的行为自动量化。本短期课程将为与会者提供 （面对面和虚拟）;关于最先进的基于机器学习的行为量化的信息， 行为量化的基本原理，实践研讨会和数据分析，学生教师论坛 网络互动，以及计算行为学前沿的培训。学生将从 该课程具有以下能力：1）设计高质量，足够动力的行为实验; 2）选择和 安装合适的平台，对动物行为进行高分辨率分析; 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，并验证 保留数据;以及4）运行在提取之后分析其数据所必需的工作流/管道。到 为了实现这一目标，我们提出：目标1。制定并举办一个为期4天的讲习班，培训科学家如何应用 从机器学习到动物行为量化目标2.为了创造一个环境， 定量行为分析，通过促进想法的产生、讨论和协作， 发现，更广泛的应用和先进的技术发展。目标3.促进招聘， 发展神经科学，行为遗传学和定量分析的各种初级研究人员， 动物行为