Abstract

There has been a recent and substantial increase in the use of open-source tools for conducting research studies in neuroscience. The OpenBehavior Project was created to disseminate open-source projects specific to the study of behavior. In this commentary, we emphasize the benefits of adopting an open-source mindset and highlight current methods and projects that give promise for open-source tools to drive advancement of behavioral measurement and ultimately understanding the neural basis of behavior. Over the past decade, there has been an explosion in the use of new neurobiological tools for measuring and controlling brain cell activity. Recent developments in optogenetics, chemogenetics, cellular imaging, and fiber photometry have spiked publications across cellular, systems, and behavioral neuroscience. Researchers with expertise in molecular biology or cellular physiology are now carrying out behavioral studies, and often bring a fresh approach to the fine-grained study of behavior that has led to the development of many new assays for measuring behavior and cognition in animal models (mice, flies, worms, etc.). Thanks to a revolution in low-cost methods for 3D printing and off-the-shelf microcontrollers (e.g., Arduino, Teensy, microPython) and single-board computers (Raspberry Pi), many of these research groups are able to create complex behavioral tasks quite easily. The R and Python languages, specialized computing libraries (e.g., numpy, OpenCV, TensorFlow), and the Anaconda Python distribution have been crucial for the development of open source analysis software for neuroscience projects. In parallel, these developments in neuroscience research have occurred during a time when there is a simultaneous movement toward sharing computer code (Eglen et al., 2017; Gleeson et al., 2017), through websites like GitHub, and opening up the process of software and hardware design to non-experts through hackerspaces and makerspaces. Despite these developments, there is still room for …

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