The broader impact/commercial potential of this I-Corps project is the promise to revolutionize bio-medical research via the development of machine vision algorithms for automating video analysis and behavioral monitoring. Many areas of the life sciences demand the manual annotation of large amounts of video data. However, the robust quantification of complex behaviors imposes a major bottleneck and a number of controversies in behavioral studies have arisen because of the inherent biases and challenges associated with the manual annotation of behavior. Many of these issues will be resolved with the use of objective quantitative computerized techniques. The goal of the project is to leverage machine learning and computer vision to analyze large volumes of data and discover novel visual features of behavior that are literally hidden to the naked eye.
This I-Corps project proposes the large-scale development, testing, and research application of algorithms and software for automating the monitoring and analysis of behavior. We have developed an initial high-throughput system for the automated monitoring and analysis of rodent behavior. The approach capitalizes on recent developments in the area of deep learning, which is a branch of machine learning that enables neural networks composed of multiple processing stages to learn visual representations with multiple levels of abstraction. The current system accurately recognizes a myriad of normal and abnormal rodent behaviors at a level indistinguishable from human when scoring typical behaviors of a singly housed mouse from video. The proposed activities will bring algorithms closer to commercial deployment by addressing the fundamental problem of visual recognition in biological, cognitive, and psychological research.
