Abstract Body

Sharp wave-ripples (SWRs) are high-frequency oscillatory events involved in the dialogue between the hippocampus and cortical regions to promote memory consolidation during sleep. Many studies aiming to investigate the role of SWRs in mnemonic processes employ closed-loop strategies to detect those events and manipulate different brain areas in real-time. However, the codes and schematics necessary to replicate the detection system are not always available, which contributes negatively to the reproducibility of experiments among different research groups. Furthermore, information about the performance is not usually reported. We present the development and validation of an open-source, real-time SWR detection plugin integrated to the Open Ephys acquisition system. It contains a built-in movement detector based on accelerometer or electromyogram data that prevents false ripple events (due to chewing, grooming or moving, for instance) from triggering the stimulation/manipulation device. To determine the accuracy of the detection algorithm we first carried out simulations in Matlab with synthetic and real ripple recordings. Using a specific combination of detection parameters, we obtained 97.9% of true positive rate and 2.29 false positives per minute on the real data. Next, an Open Ephys plugin based on the same detection algorithm was developed and a closed-loop system was set up to evaluate the round trip (ripple onset-to-stimulation) latency over synthetic data. The lowest latencies obtained were around 35 ms. Besides contributing to increased reproducibility, we anticipate that the developed SWR detector plugin will be useful for a number of closed-loop applications in the field of Systems Neuroscience.