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<h1><strong> REC-GUI </h1></strong> This project contains the code for the Real-Time Experimental Control with Graphical User Interface (REC-GUI). For up to date code and documentation visit our [GitHub repository]( or the [REC-GUI website]( for more information. <h3> Overview </h3> The Real-time Experimental Control with Graphical User Interface (REC-GUI) was created by the **[Rosenberg Lab](** at the University of Wisconsin-Madison. Modern neuroscience research often requires the coordination of multiple processes such as stimulus generation, real-time experimental control, as well as behavioral and neural measurements. The technical demands required to simultaneously manage these processes with high temporal fidelity limits the number of labs capable of performing such work. Here we present an open-source network-based parallel processing framework that lowers this barrier. The Real-Time Experimental Control with Graphical User Interface (REC-GUI) Framework offers multiple advantages, including: (i) a modular design that is agnostic to coding language(s) and operating system(s) to maximize experimental flexibility and minimize researcher effort, (ii) simple interfacing to connect multiple measurement and recording devices, (iii) high temporal fidelity by dividing task demands across CPUs, and (iv) real-time control using a fully customizable and intuitive GUI. We present applications for human, non-human primate, and rodent studies which collectively demonstrate that the REC-GUI Framework facilitates technically demanding, behavior-contingent neuroscience research. Sample code and hardware configurations are downloadable, and future developments will be released. Tutorials for learning how to use the software are provided in the Documentation folder. Video tutorials accompanying the documentation can be found on our [YouTube channel]( --- <h3> Citations </h3> If using REC-GUI, please use the following citation and Research Resource Identifier (RRID). Kim B, Kenchappa SC, Sunkara A, Chang TY, Thompson L, Doudlah R, and Rosenberg A (2019) Real-time experimental control using network-based parallel processing. eLife, 8: [e40231]( RRID: [SCR_019008](
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