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Rodent Arena Tracker (RAT)

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



August 1, 2019

In their recent eNeuro article, Bridget Matikainen-Ankney and colleagues from the Kravitz Lab have developed and shared their device, rodent activity detector (RAD), a low-cost system that can track and record activity in rodent home cages.

Physical activity is an important measure used in many research studies and is an important determinant of human health. Current methods for measuring physical activity in laboratory rodents have limitations including high expense, specialized caging/equipment, and high computational overhead. To address these limitations, Matikainen-Ankney et al. designed an open-source and cost-effective device for measuring rodent behavior.

In their new manuscript, they describe the design and implementation of RAD, rodent activity detector. The system allows for high throughput installation, minimal investigator intervention and circadian monitoring.  The design includes a battery powered passive infrared (PIR) sensor, microcontroller, microSD card logger, and an oLED screen for displaying data. All of the build instructions for RAD manufacture and programming, including the Arduino code, are provided on the project’s website.

The system records the number of PIR active bouts and the total duration the PIR is active each minute. The authors report that RAD is useful for quantifying changes across minutes rather than on a second to second time-scale, so the default data-logging frequency is set to one minute. The CSV files can be viewed and data visualized using provided python scripts. Device validation with video monitoring strongly correlated PIR data with speed and showed it recorded place to place locomotion but not slow or in place movements. To verify the device’s utility, RAD was used to collect physical activity data from 40 animals for 10 weeks. RAD detected high fat diet (HFD)-induced changes in activity and quantified individual animals’ circadian rhythms. Several major advantages of this tool are that the PIR sensor is not triggered by activity in other cages, it can detect and quantify within-mouse activity changes over time, and little investigator intervention other than infrequent battery replacement is necessary. Although the design was optimized for the lab’s specific caging, the open-source nature of the project makes it easily modifiable.

More details on RAD can be found in their eNeuro manuscript here, and all documentation can also be found on the project’s Hackaday.io page.

Matikainen-Ankney, B. A., Garmendia-Cedillos, M., Ali, M., Krynitsky, J., Salem, G., Miyazaki, N. L., … Kravitz, A. V. (2019). Rodent Activity Detector (RAD), an Open Source Device for Measuring Activity in Rodent Home Cages. ENeuro, 6(4). https://doi.org/10.1523/ENEURO.0160-19.2019


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


April 17, 2019

In a recent Nature Protocol’s article, Edoardo Balzani and colleagues from Valter Tucci’s lab have developed and shared Phenopy, a Python-based open-source analytical platform for behavioral phenotyping.

Behavioral phenotyping of mice using classic methods can be a long process and is susceptible to high variability, leading to inconsistent results. To reduce variance and speed up to process of behavioral analysis, Balzani et al. developed Phenopy, an open-source software for recording and analyzing behavioral data for phenotyping. The software allows for recording components of a behavioral task in combination with electrophysiology data. It is capable of performing online analysis as well as analysis of recorded data on a large scale, all within a user-friendly interface. Information about the software is available in their publication, available from Nature Protocols.*

Check out the full article from Nature Protocols!

(*alternatively available on ResearchGate)


February 20, 2019

Francisco Romero Ferrero and colleagues have developed idtracker.ai, an algorithm and software for tracking individuals in large collectives of unmarked animals, recently described in Nature Methods.

Tracking individual animals in large collective groups can give interesting insights to behavior, but has proven to be a challenge for analysis. With advances in artificial intelligence and tracking software, it has become increasingly easier to collect such information from video data. Ferrero et al. have developed an algorithm and tracking software that features two deep networks. The first tracks animal identification and the second tracks when animals touch or cross paths in front of one another. The software has been validated to track individuals with high accuracy in cohorts of up to 100 animals with diverse species from rodents to zebrafish to ants. This software is free, fully-documented and available online with additional jupyter notebooks for data analysis.

Check out their website with full documentation, the recent Nature Methods article, BioRXiv preprint, and a great video of idtracker.ai tracking 100 zebrafish!

PsiBox: Automated Operant Conditioning in the Mouse Home Cage

November 30, 2018

Nikolas Francis and Patrick Kanold of the University of Maryland share their design for Psibox, a platform for automated operant conditioning in the mouse home cage, in Frontiers in Neural Circuits.

The ability to collect behavioral data from large populations of subjects is advantageous for advancing behavioral neuroscience research. However, few cost-effective options are available for collecting large sums of data especially for operant behaviors. Francis and Kanold have developed and shared Psibox,  an automated operant conditioning system. It incorporates three modules for central control , water delivery, and home cage interface, all of which can be customized with different parts. The system was validated for training mice in a positive reinforcement auditory task and can be customized for other tasks as well. The full, low-cost system allows for quick training of groups of mice in an operant task with little day-to-day experimenter involvement.

Learn how to set up your own Psibox system here!

Francis, NA., Kanold, PO., (2017). Automated operant conditioning in the mouse home cage. Front. Neural Circuits.