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.

项目概要/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，定量行为分析已大大加速 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。该技术具有广泛 当前和潜在的应用将影响与直接相关和影响的广泛领域 人类健康和疾病的研究，包括神经科学、行为、遗传学、精神病学和 生物医学。然而，一些障碍限制了这些工具和分析的广泛采用。首先，很多 跟踪和行为分析包需要高水平的计算专业知识，因此受到限制 向专家实验室申请。其次，对于高分辨率数据流，定量行为需要新的 统计工具和正确的数据建模。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，我们认识到各个相关领域的研究人员的需求尚未得到满足 学习其严格使用的基础知识。因此，培养新一代跨学科研究人员 结合神经科学、机器学习和行为的接口，我们建议建立每年 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 为定量神经行为学和行为领域提供实用的介绍：我们提出 独特且及时的跨学科课程《机器学习应用短期课程》 杰克逊实验室 (JAX) 的行为自动量化。该短期课程将为与会者提供 （面对面和虚拟地）与；有关基于机器学习的行为定量的最先进信息， 行为量化基础知识、实践研讨会和数据分析、师生论坛 网络交互，以及计算行为学前沿的培训。学生们将从中脱颖而出 该课程具有以下能力：1）设计高质量、动力充足的行为实验； 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 策略，包括收集新的训练数据集、训练分析软件以及验证性能 保留的数据； 4) 运行提取后分析数据所需的工作流程/管道。到 为了实现这一目标，我们建议： 目标 1. 制定并举办为期 4 天的研讨会，培训科学家应用 机器学习到动物行为定量。目标 2. 创造一个扩大领域的环境 通过促进想法产生、讨论和协作来产生新的定量行为分析 发现、更广泛的应用和先进的技术发展。目标 3. 促进招聘和 神经科学、行为遗传学和定量分析领域多样化初级研究人员的发展 动物行为。