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Specifically, we developed an application for the widely used Microsoft Windows operating system, based on an inexpensive general purpose, 64-channel data acquisition board and a single, generic personal computer (PC). Given this state of things, we sought to develop a closed-loop system, which addresses some of the shortcomings mentioned above. Unfortunately, this is not always the case, and many of such systems represent tradeoffs between performance, complexity, ease of development, ease of use, expandability, dependence on specialized vs. Preferably, such systems would be inexpensive, expandable, and customizable, easy to use, and provide user-friendly interfaces and development environments. Indeed, quite a few systems, based on different platforms, have been developed for this purpose. The advent of new stimulation and manipulation modalities (e.g., optogenetics) opens new and exciting possibilities for studying neuronal network functions or controlling network activity (e.g., Fong et al., 2015 Newman et al., 2015), increasing the need for fast, multichannel closed-loop control systems. The use of closed-loop control systems, however, has expanded far beyond this purpose (reviewed in Wallach, 2013 Krook-Magnuson et al., 2015 Wright et al., 2016 see also Potter et al., 2014 and references therein). The best-known example in the Neurosciences is the Voltage Clamp system, in which the control of membrane potential provides the means for resolving the dependence of ion-channel conductance on membrane potential. We then demonstrate, using networks of cortical neurons growing on multielectrode arrays (MEA) that despite its reliance on generic hardware, its performance is appropriate for flexible, closed-loop experimentation at the neuronal network level.Ī common manner for studying complex dynamic systems is to use closed-loop systems that fix or control one or more interdependent variables by uncoupling interdependent variables, such closed-loop systems allow these interdependencies to be resolved. We describe the application, its architecture and facilities. Importantly, it includes facilities for running closed-loop protocols written in any programming language that can generate dynamic link libraries (DLLs). CLEM provides a fully functional, user-friendly graphical interface, possesses facilities for recording, presenting and logging electrophysiological data from up to 64 analog channels, and facilities for controlling external devices, such as stimulators, through digital and analog interfaces. CLEM is an open source, soft real-time, Microsoft Windows desktop application that is based on a single generic personal computer (PC) and an inexpensive, general-purpose data acquisition board. We then present our own solution, which we refer to as Closed Loop Experiments Manager (CLEM). Here, we survey commercial and open source systems that address these needs to varying degrees. Finally, they should provide powerful, yet reasonably easy to implement facilities for developing closed-loop protocols for interacting with neuronal systems. Furthermore, there is a need for such systems to be inexpensive, reliable, user friendly, easy to set-up, open and expandable, and possess long life cycles in face of rapidly changing computing environments. Such systems are needed, for example, for closed loop, multichannel electrophysiological/optogenetic experimentation in vivo and in a variety of other neuronal preparations, or for developing and testing neuro-prosthetic devices, to name a few. There is growing need for multichannel electrophysiological systems that record from and interact with neuronal systems in near real-time.
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