[
International Worm Meeting,
2017]
The overall goal of this project is to develop an imaging system with machine learning capabilities to aid in the study of how genes and neural circuits give rise to animal behavior. Our secondary mission was to create a complete imaging system that was low enough in cost for labs to use many devices in parallel, or for high school and college classrooms to be able to conduct imaging-based biological experiments. Imaging equipment has increased in quality and decreased in cost to a point in which we were able to build an ultra-low-cost imaging system for recording animal behavior which could accomplish our objectives. Specifically, the system is optimized for recording locomotion of the genetic model organism C. elegans on a near-flat translucent surface. We utilized the free programming language Python with machine learning packages to incorporate automatic analysis of the recorded videos. Several machine learning algorithms for classifying and annotating animal behavior were tested against the performance of human experts, and the top performing algorithms are implemented in the final software. This system has the potential to save researchers time and money and allow them to quickly determine how manipulating genes and neural circuits alters animal behavior. Future plans include adapting the system for other organisms and more complex behaviors.