Inexpensive, scalable camera system for tracking rats in large spaces
AbstractMost studies focused on understanding the neural circuits underlying spatial navigation are restricted to small behavioral arenas ([≤] 1 m2) because of the limits imposed by the cables extending from the animal to the recording system. New wireless recording systems have significantly increased the recording range. However, the size of arena is still constrained by the lack of a video tracking system capable of monitoring the animals movements over large areas integrated with these recording systems. We developed and benchmarked a novel, open-source, scalable multi-camera tracking system based on commercially available and low-cost hardware (Raspberry Pi computers and Raspberry Pi cameras). This Picamera system was used in combination with a wireless recording system for characterizing neural correlates of space in environments of various sizes up to 16.5 m2. Spatial rate maps generated using the Picamera system showed improved accuracy in estimating spatial firing characteristics of neurons compared to a popular commercial system, due to its better temporal accuracy. The system also showed improved accuracy in estimating head direction cell tuning as well as theta phase precession in place cells. This improved temporal accuracy is crucial for accurately aligning videos from multiple cameras in large spaces and characterizing spatially modulated cells in a large environment.