Most studies of neural correlates of spatial navigation are restricted to small arenas (≤ 1 m) because of the limits imposed by the recording cables. New wireless recording systems have a larger recording range. However, these neuronal recording systems lack the ability to track animals in large area, constraining the size of the arena. We developed and benchmarked an open-source, scalable multi-camera tracking system based on low-cost hardware. This "Picamera system" was used in combination with a wireless recording system for characterizing neural correlates of space in environments of sizes up to 16.5 m. The Picamera system showed substantially better temporal accuracy than a popular commercial system. An explicit comparison of one camera from the Picamera system with a camera from the commercial system showed improved accuracy in estimating spatial firing characteristics and head direction tuning of neurons. 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.
Most 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 animal’s 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.
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