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

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.