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<p>Behavioral neuroscience relies heavily on controlled environments, such as operant chambers or “Skinner boxes,” to characterize relationships between external stimuli and the resulting animal behavior. Increasingly, these methodologies are critical for the development of neural interfaces which seek to provide or restore sensations via electrical stimulation. To conduct behavioral experiments, researchers have commonly trusted commercial systems, like those from Med Associates, Inc. While offering reliability, high costs and limited customizability have motivated a push towards open-source alternatives, which often involve the use of inexpensive microcontrollers, custom printed circuit boards (PCBs), and freely available codebases. However, despite these developments, there is a lack of comprehensive software solutions that can integrate seamlessly with commercial or custom hardware for behavioral experiments. In this study, we developed a full-stack application utilizing Angular and Flask frameworks to conduct two-alternative forced choice (2AFC) tasks controlled by an Arduino which interfaces with Med Associates, Inc. operant chamber equipment via a custom PCB. The system was tested by conducting a simple operant conditioning procedure and a spinal cord stimulation (SCS) sensory detection experiment using a custom microstimulator in rodents. The analyzed data demonstrated appropriate behavioral learning and sensory detection thresholds, in alignment with previous SCS behavioral studies which utilized commercial or single-tier systems for control of operant chambers. This work demonstrates the effective integration of an open-source full-stack application with existing commercial hardware that can provide adaptable and scalable means for conducting behavioral experiments, crucial for advancing neural interface technologies.</p>

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Low-cost, open-source, full-stack software and Arduino-based hardware for control of commercially available animal behavior systems

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月29日

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  • Scott Miller,
  • Jacob C. Slack,
  • Amol P. Yadav

摘要

Behavioral neuroscience relies heavily on controlled environments, such as operant chambers or “Skinner boxes,” to characterize relationships between external stimuli and the resulting animal behavior. Increasingly, these methodologies are critical for the development of neural interfaces which seek to provide or restore sensations via electrical stimulation. To conduct behavioral experiments, researchers have commonly trusted commercial systems, like those from Med Associates, Inc. While offering reliability, high costs and limited customizability have motivated a push towards open-source alternatives, which often involve the use of inexpensive microcontrollers, custom printed circuit boards (PCBs), and freely available codebases. However, despite these developments, there is a lack of comprehensive software solutions that can integrate seamlessly with commercial or custom hardware for behavioral experiments. In this study, we developed a full-stack application utilizing Angular and Flask frameworks to conduct two-alternative forced choice (2AFC) tasks controlled by an Arduino which interfaces with Med Associates, Inc. operant chamber equipment via a custom PCB. The system was tested by conducting a simple operant conditioning procedure and a spinal cord stimulation (SCS) sensory detection experiment using a custom microstimulator in rodents. The analyzed data demonstrated appropriate behavioral learning and sensory detection thresholds, in alignment with previous SCS behavioral studies which utilized commercial or single-tier systems for control of operant chambers. This work demonstrates the effective integration of an open-source full-stack application with existing commercial hardware that can provide adaptable and scalable means for conducting behavioral experiments, crucial for advancing neural interface technologies.