June 18, 2020
Jonathan Krynitsky and colleagues from the Kravitz lab at Washington University have constructed and shared RAT, a closed loop system for machine vision rodent tracking and task control.
The Rodent Arena Tracker, or RAT, is a low cost wireless position tracker for automatically tracking mice in high contrast arenas. The device can use subject position information to control other devices in real time, allowing for closed loop control of various tasks based on positional behavior data. The device is based on the OpenMV Cam M7 (openmv.io), an opensource machine vision camera equipped with onboard processing for real-time analysis which reduces data storage requirements and removes the need for an external computer. The authors optimized the control code for tracking mice and created a custom circuit board to run the device off a battery and include a real-time clock for synchronization, a BNC input/output port, and a push button for starting the device. The build instructions for RAT, as well as validation data to highlight effectiveness and potential uses for the device are available in their recent publication. Further, all the design files, such as the PCB design, 3D printer files, python code, etc, are available on hackaday.io.
Read the full article here!
Or check out the project on hackaday.io!
Krynitsky, J., Legaria, A. A., Pai, J. J., Garmendia-Cedillos, M., Salem, G., Pohida, T., & Kravitz, A. V. (2020). Rodent Arena Tracker (RAT): A Machine Vision Rodent Tracking Camera and Closed Loop Control System. Eneuro, 7(3). doi:10.1523/eneuro.0485-19.2020
May 16, 2019
In the Journal of Neuroscience Methods, Bastijn van den Boom and colleagues have shared their ‘how-to’ instructions for implementing behavioral classification with JAABA, featuring bonsai and motr!
In honor of our 100th post on OpenBehavior, we wanted to feature a project that exemplifies how multiple open-source projects can be implemented to address a common theme in behavioral neuroscience: tracking and classifying complex behaviors! The protocol from Van den Boom et al. implements JAABA, an open-source machine learning based behavior detection system; motr, an open-source mouse trajectory tracking software; and bonsai, an open-source system capable of streaming and recording video. Together they use these tools to process videos of mice performing grooming behaviors in a variety of behavioral setups.
They then compare multiple tools for analyzing grooming behavior sequences in both wild-type and genetic knockout mice with a tendency to over groom. The JAABA trained classifier outperforms the commercially available behavior analysis software and more closely aligns with manual analysis of behavior by expert observers. This offers a novel, cost-effective and easy to use method for assessing grooming behavior in mice comparable to that of an expert observer, with the efficient advantage of being automatic. How to instructions for how to train your own JAABA classifier can be found in their paper!
Read more in their publication here!
Boom, B. J., Pavlidi, P., Wolf, C. J., Mooij, A. H., & Willuhn, I. (2017). Automated classification of self-grooming in mice using open-source software. Journal of Neuroscience Methods, 289, 48-56. doi:10.1016/j.jneumeth.2017.05.026
May 10, 2019
In a recently published article (Erskine et al., 2019), The Schaefer lab at the Francis Crick Institute introduced their new open-source project called AutonoMouse.
AutonoMouse is a fully automated, high-throughput system for self-initiated conditioning and behavior tracking in mice. Many aspects of behavior can be analyzed through having rodents perform in operant conditioning tasks. However, in operant experiments, many variables can potentially alter or confound results (experimenter presence, picking up and handling animals, altered physiological states through water restriction, and the issue that rodents often need to be individually housed to keep track of their individual performances). This was the main motivation for the authors to investigate a way to completely automate operant conditioning. The authors developed AutonoMouse as a fully automated system that can track large numbers (over 25) of socially-housed mice through implanted RFID chips on mice. With the RFID trackers and other analyses, the behavior of mice can be tracked as they train and are subsequently tested on (or self-initiate testing in) an odor discrimination task over months with thousands of trials performed every day. The novelty in this study is the fully automated nature or the entire system (training, experiments, water delivery, weighing the animals are all automated) and the ability to keep mice socially-housed 24/7, all while still training them and tracking their performance in an olfactory operant conditioning task. The modular set-up makes it possible for AutonoMouse to be used to study many other sensory modalities, such as visual stimuli or in decision-making tasks. The authors provide a components list, layouts, construction drawings, and step-by-step instructions for the construction and use of AutonoMouse in their publication and on their project’s github.
For more details, check out this youtube clip interview with Andreas Schaefer, PI on the project.
The github for the project’s control software is located here: https://github.com/RoboDoig/autonomouse-control and for the project’s design and hardware instructions is here: https://github.com/RoboDoig/autonomouse-design. The schedule generation program is located here: https://github.com/RoboDoig/schedule-generator