Abstract
With the recent development and rapidly accelerating adoption of machine-learning based rodent behavioral tracking tools such as DeepLabCut, one common variable that can impact the quality and consistency of results is the camera system. Many experimenters use webcams, GoPros, or other commercially available cameras that are not only relatively expensive, but offer very little flexibility over recording parameters. These cameras are not optimized for recording many types of behavioral experiments, which can lead to suboptimal video quality. Furthermore, it is a challenge, if not impossible, to synchronize multiple cameras with each other, or to send/receive a trigger with external signals such as a TTL pulse or a network connection. We have developed an affordable ecosystem of behavioral recording equipment, PiRATeMC (Pi-based Remote Acquisition Technology for Motion Capture), that relies on Raspberry Pi Camera Boards that are ideal for recording in both bright light, low light, and dark conditions under infrared light. PiRATeMC offers users control over nearly every recording parameter. This setup can easily be scaled up and synchronously controlled in clusters via a self-contained network to record a large number of simultaneous behavioral sessions without burdening institutional network infrastructure. Furthermore, the Raspberry Pi is an excellent platform for novice and inexperienced programmers interested in using an open-source recording system, with a large online community that is very active in developing novel open-source tools. Moreover, it easily interfaces with Arduinos and other microcontrollers, allowing simple synchronization and interfacing of video recording with nearly any behavioral equipment using GPIO pins to send or receive 3.3V or 5V signals, I2C, or serial communication.
Competing Interest Statement
The authors have declared no competing interest.