Centanni Samuel W, Smith Alexander C W
Department of Physiology and Pharmacology, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA.
Department of Neuroscience, Medical University of South Carolina, Charleston, SC 29412, USA.
Addict Neurosci. 2023 Dec;8. doi: 10.1016/j.addicn.2023.100108. Epub 2023 Jun 17.
With the rapidly accelerating adoption of machine-learning based rodent behavioral tracking tools, there is an unmet need for a method of acquiring high quality video data that is scalable, flexible, and relatively low-cost. Many experimenters use webcams, GoPros, or other commercially available cameras that can be expensive, offer minimal flexibility of recording parameters, and not optimized for recording rodent behavior, leading to suboptimal and inconsistent video quality. Furthermore, commercially available products are not conducive for synchronizing multiple cameras, or interfacing with third-party equipment to allow time-locking of video to other equipment such as microcontrollers for closed-loop experiments. We present a low-cost, customizable ecosystem of behavioral recording equipment, PiRATeMC (Pi-based Remote Acquisition Technology for Motion Capture) based on Raspberry Pi Camera Boards with the ability to acquire high quality recordings in bright/low light, or dark conditions under infrared light. PiRATeMC offers users control over nearly every recording parameter, and can be fine-tuned to produce optimal videos in any behavioral apparatus. This setup can be scaled up for synchronous control of any number of cameras via a self-contained network without burdening institutional network infrastructure. The Raspberry Pi is an excellent platform with a large online community designed for novice and inexperienced programmers interested in using an open-source recording system. Importantly, PiRATeMC supports TTL and serial communication, allowing for synchronization and interfacing of video recording with behavioral or other third-party equipment. In sum, PiRATeMC minimizes the cost-prohibitive nature of conducting and analyzing high quality behavioral neuroscience studies, thereby increasing accessibility to behavioral neuroscience.
随着基于机器学习的啮齿动物行为跟踪工具的迅速普及，对于一种可扩展、灵活且成本相对较低的获取高质量视频数据的方法存在未满足的需求。许多实验者使用网络摄像头、GoPro或其他市售相机，这些相机可能很昂贵，录制参数的灵活性极小，且未针对记录啮齿动物行为进行优化，导致视频质量欠佳且不一致。此外，市售产品不利于同步多个摄像头，也不利于与第三方设备接口，以便将视频与其他设备（如用于闭环实验的微控制器）进行时间锁定。我们提出了一种低成本、可定制的行为记录设备生态系统，即PiRATeMC（基于树莓派的运动捕捉远程采集技术），它基于树莓派相机板，能够在明亮/低光照或红外光下的黑暗条件下获取高质量记录。PiRATeMC允许用户控制几乎所有的录制参数，并且可以进行微调以在任何行为装置中生成最佳视频。这种设置可以通过一个独立的网络进行扩展，以同步控制任意数量的摄像头，而不会给机构网络基础设施带来负担。树莓派是一个出色的平台，有一个庞大的在线社区，专为对使用开源记录系统感兴趣的新手和经验不足的程序员设计。重要的是，PiRATeMC支持TTL和串行通信，允许视频记录与行为或其他第三方设备进行同步和接口。总之，PiRATeMC将进行和分析高质量行为神经科学研究的成本过高的性质降至最低，从而增加了行为神经科学的可及性。
