RT Journal Article SR Electronic T1 How Do Spike Collisions Affect Spike Sorting Performance? JF eneuro JO eNeuro FD Society for Neuroscience SP ENEURO.0105-22.2022 DO 10.1523/ENEURO.0105-22.2022 VO 9 IS 5 A1 Garcia, Samuel A1 Buccino, Alessio P. A1 Yger, Pierre YR 2022 UL http://www.eneuro.org/content/9/5/ENEURO.0105-22.2022.abstract AB Recently, a new generation of devices have been developed to record neural activity simultaneously from hundreds of electrodes with a very high spatial density, both for in vitro and in vivo applications. While these advances enable to record from many more cells, they also challenge the already complicated process of spike sorting (i.e., extracting isolated single-neuron activity from extracellular signals). In this work, we used synthetic ground-truth recordings with controlled levels of correlations among neurons to quantitatively benchmark the performance of state-of-the-art spike sorters focusing specifically on spike collisions. Our results show that while modern template-matching-based algorithms are more accurate than density-based approaches, all methods, to some extent, failed to detect synchronous spike events of neurons with similar extracellular signals. Interestingly, the performance of the sorters is not largely affected by the spiking activity in the recordings, with respect to average firing rates and spike-train correlation levels. Since the performances of all modern spike sorting algorithms can be affected as function of the activity of the recorded neurons, scientific claims on correlations and synchrony should be carefully assessed based on the analysis provided in this paper.