High-throughput, high-resolution 3D measurement of ethologically relevant rodent behavior in a dynamic environment - Summary The aim of this proposal is to develop an innovative new system, including hardware assemblies and machine learning algorithms, for continuous, high-resolution 3D quantification of behavioral and eliciting stimulus dynamics in a natural mouse prey capture paradigm. The system will satisfy a critical unmet need for an easily adoptable, modern behavioral measurement technology that extends well beyond current offerings, which are difficult to set up and limited largely to measuring spontaneous animal movement in impoverished, static environments. Our system consists of a 3D convolutional neural network processing multi-perspective video recordings to provide detailed measurements of both predator (mouse) and prey (cricket) spatiotemporal movement patterns within an enclosed, compact apparatus permitting precise control over the visual environment. To reduce implementation complexity and enhance usability in other labs, the system will use only a single commercial video camera and a set of low-cost mirrors to provide the multiple perspectives required for 3D behavior tracking. By using only a single camera, we also reduce the instrument’s physical footprint, thus facilitating high-throughput studies across multiple setups. Furthermore, our 3D tracking algorithm will be built to support out-of-the-box generalization to cloned setups, meaning other labs can immediately start doing science with the instrument without laborious data labeling and training steps. As part of our system, we will also develop new methods for analyzing the rich 3D mouse and cricket data to isolate key kinematic and action variables along with comprehensive characterization of stimulus-behavior relationships. We will then investigate how these new measurements can be used to better understand retinitis pigmentosa and Parkinson’s disease. Preliminary experiments have been quite successful and illustrate the promise and power of our approach to collect large amounts of quantitative behavior data and identify new phenotypes of motor disorders. As our vision is to make as large of an impact as possible, our system and datasets will be shared openly with community to catalyze a wide range of new research into brain function and treatments for neurological disease.
