December 21, 2016
David Barker from the National Institute on Drug Abuse Intramural Research Program has shared the following regarding the development of a device designed to allow the automatic detection of 50kHz ultrasonic vocalizations.
Ultrasonic vocalizations (USVs) have been utilized to infer animals’ affective states in multiple research paradigms including animal models of drug abuse, depression, fear or anxiety disorders, Parkinson’s disease, and in studying neural substrates of reward processing. Currently, the analysis of USV data is performed manually, and thus time consuming.
The present method was developed in order to allow for the automated detection of 50-kHz ultrasonic vocalizations using a template detection procedure. The detector runs in XBAT, an extension developed for MATLAB developed by the Bioacoustics Research Program at Cornell University. The specific template detection procedure for ultrasonic vocalizations along with a number of companion tools were developed and tested by our laboratory. Details related to the detector’s performance can be found within our published work and a detailed readme file is published along with the MATLAB package on our GitHub.
Our detector was designed to be freely shared with the USV research community with the hope that all members of the community might benefit from its use. We have included instructions for getting started with XBAT, running the detector, and developing new analysis tools. We encourage users that are familiar with MATLAB to develop and share new analysis tools. To facilitate this type of collaboration, all files have been shared as part of a GitHub repository, allowing for suggested changes or novel contribution to be made to the software package. I would happily integrate novel analysis tools created by others into future releases of the detector.
Work on a detector for 22-kHz vocalizations is ongoing; the technical challenges for detecting 22-kHz vocalizations, which are nearer to audible noise, are more difficult. Those interested in contributing to this can email me at djamesbarker@gmail-dot-com or find me on twitter (@DavidBarker_PhD).
The Feeding Experimentation Device (FED) is a free, open-source system for measuring food intake in rodents. FED uses an Arduino processor, a stepper motor, an infrared beam detector, and an SD card to record time-stamps of 20mg pellets eaten by singly housed rodents. FED is powered by a battery, which allows it to be placed in colony caging or within other experimental equipment. The battery lasts ~5 days on a charge, providing uninterrupted feeding records over this duration. The electronics for building each FED cost around $150USD, and the 3d printed parts cost between $20 and $400, depending on access to 3D printers and desired print quality.
The Kravitz lab has published a large update of their Feeding Experimentation Device (FED) to their Github site (https://github.com/KravitzLab/fed), including updated 3D design files that print more easily and updates to the code to dispense pellets more reliably. Step-by-step build instructions are available here: https://github.com/KravitzLab/fed/wiki
The Laubach Lab at American University investigates executive control and decision making, focusing on the role of the prefrontal cortex. Through their GitHub repository, these researchers provide 3D print files for many of the behavioral devices used in their lab, including a Nosepoke and a Lickometer designed from rats. The repository also includes a script that reads MedPC files into Python in a usable way.
The openBehavior github repository from Hao Chen’s lab at UTHSC aims to establish a computing platform for rodent behavior research using the Raspberry Pi computer. They have buillt several devices for conducting operant conditioning and monitoring enviornmental data.
The operant licking device can be placed in a standard rat home cage and can run fixed ratio, various ratio, or progressive ratio schedules. A preprint describing this project, including data on sucrose vs water intake is available. Detailed instructions for making the device is also provided.
The environment sensor can record the temperature, humidity, barometric pressure, and illumination at fixed time intervals and automatically transfer the data to a remote server.
There is also a standard alone RFID reader for the EM4100 implantable glass chips, a motion sensor addon for standard operant chambers, and several other devices.