S3200 dark noise reduce5/24/2023 ![]() We then went on to record from a 5.5 m x 3 m arena using the Picamera system coupled with the wireless electrophysiology system to demonstrate our ability to characterize neural correlates of spatial navigation in a large space. We show that the higher temporal accuracy of the Picamera system improved our ability to estimate multiple spatial firing characteristics of spatially modulated cells in standard environments used in spatial navigation studies. ![]() We recorded different cell types from the hippocampal formation using both these video trackers coupled with the wireless electrophysiology system. To benchmark the Picamera system, we compared its performance with a commercial video tracking system sold as a part of a wireless electrophysiology system. Here, we describe a novel tracking system comprising 8 overhead Raspberry Pi cameras (referred to as the “Picamera system” henceforth), capable of tracking an animal’s position in a large environment. Wide angle lenses with higher resolution 4K cameras can alleviate the resolution issue, but occlusion by the experimenter and the environmental features, cost, and synchronization with neural recording system make this solution sub-optimal. Using wide angle lenses with the standard cameras is not a satisfactory solution, as the resolution decreases drastically. Most commercial neuronal recording systems, however, do not have provisions for recording from more than two cameras which again constrains the size of the behavioral arena, leading to an increased need for a system capable of tracking animals in larger environments. ![]() The advent of wireless recording systems enables us to now record neural activity from the hippocampal formation while the rat forages in larger and more complex environments. Home ranges of Norway rats vary from tens of m 2 in urban areas to hundreds of m 2 in farms and fields ( Lambert et al., 2008 Oyedele et al., 2015) rat burrows occupy an area of the order of 10 m 2 ( Calhoun, 1963). This experimental constraint leaves a significant lacuna in our understanding of the neural correlates of spatial navigation in environments of scale and complexity comparable to the natural habitat of the rat. Over the years, a variety of cell types such as grid cells ( Hafting et al., 2005), head direction cells ( Ranck, 1984 Taube et al., 1990), speed cells ( Kropff et al., 2015), border cells ( Savelli et al., 2008 Solstad, et al., 2008), object cells ( Deshmukh & Knierim, 2011), and landmark vector cells ( Deshmukh & Knierim, 2013) have been recorded from the hippocampal formation, advancing our understanding of its role in spatial navigation.īecause of the limits imposed by the cables extending from the animal to the recording system, these experiments studying spatial maps have largely been limited to small spaces (≤ 1 m 2), with rare exceptions (for example, 1.5 m x 1.4 m in Fenton et al., 2008 1.8 m x 1.4 m in Park et al., 2011 2.2 m x 2.2 m in Stensola et al., 2012 3.5 m diameter circular arena in Gothard et al., 1996 18 m track in Kjelstrup et al., 2008 48 m track in Rich et al., 2014). Since the discovery of place cells in the hippocampus four decades ago ( O’Keefe & Dostrovsky, 1971 O’Keefe & Nadel, 1978), a considerable amount of work has been undertaken to understand the representation of space in the brain. Spatial navigation is a widely employed behavior to study the neuronal circuits underlying cognition, learning, and memory. 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. The system also showed improved accuracy in estimating head direction cell tuning as well as theta phase precession in place cells. 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. 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 m 2. 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). 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. New wireless recording systems have significantly increased the recording range. Most studies focused on understanding the neural circuits underlying spatial navigation are restricted to small behavioral arenas (≤ 1 m 2) because of the limits imposed by the cables extending from the animal to the recording system.
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