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An important prerequisite for the analysis of spike synchrony inextracellular recordings is the extraction of single unit activity from therecorded multi unit signal. To identify single units (SUs), potential spikesare detected and separated with respect to their potential neuronal origins('spike sorting'). However, different sorting algorithms yield inconsistentunit assignments which seriously influences the subsequent analyses of thespiking activity. To evaluate the quality of spike sortings performed withdifferent prevalent algorithms offered by the 'Plexon Offline Sorter' we firstapply these algorithms to experimental data (ED), namely recordings in theSubthalamic Nucleus of patients with Parkinson's disease, obtained during DeepBrain Stimulation surgery. Since this procedure leaves us unsure about the bestsorting result we then apply all methods again to artificial data (AD) withknown ground truth (GT). AD consists of pairs of SUs with different shapesimilarity embedded in the background noise of the ED. The sorting evaluationis based on the influence of the respective methods on the SU assignments andits effect on the resulting firing characteristics. We find a high variabilityin the sorting results obtained by different algorithms that increases with SUshape similarity. We also find significant differences in the resulting firingcharacteristics of the ED. We conclude that Valley-Seeking produces the mostaccurate results if the exclusion of artifacts as unsorted events is important.If the latter is less important ('clean' data) K-Means is a better option. Ourresults strongly argue for the need of standardized validation procedures forspike sorting based on GT data. The recipe suggested here is simple enough tobecome a standard procedure.

Evaluation of spike sorting algorithms: Simulations and application to human Subthalamic Nucleus recordings