From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings - Bod, Réka Barbara; Rokai, János; Meszéna, Domokos; Fiáth, Richárd; Ulbert, István; Márton, Gergely (Frontiers in Neuroinformatics, 2022)
To discover meaning behind neural single unit activity has constantly been a challenge – and so it persists for the foreseeable future. As one of the most sourced strategies, detecting neural activity on high-resolution neural sensor recordings, then attributing them to their corresponding source neurons correctly, namely the process of spike sorting has been prevailing so far. Supported by ever-improving recording techniques and sophisticated algorithms for extracting worthwhile information, as well as the abundance in clustering procedures turned spike sorting into an indispensable tool in electrophysiological analysis. This review attempts to illustrate that in all stages of spike sorting algorithms, past five years’ innovations brought about concepts, results and questions worth sharing with even the non-expert user community. By thoroughly inspecting latest innovations in the field of neural sensors, recording procedures and various spike sorting strategies, a skeletonization of relevant knowledge lays here, with initiative to get one step closer to the original objective: deciphering and building upon the sense of neural transcript.