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.

项目总结/文摘