From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings

Abstract

The meaning behind neural single unit activity has constantly been a challenge, so it will persist in the foreseeable future. As one of the most sourced strategies, detecting neural activity in high-resolution neural sensor recordings and then attributing them to their corresponding source neurons correctly, namely the process of spike sorting, has been prevailing so far. Support from ever-improving recording techniques and sophisticated algorithms for extracting worthwhile information and 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, the past 5 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 an initiative to get one step closer to the original objective: deciphering and building in the sense of neural transcript.

Functional clustering of neuronal signals with FMM mixture models

“…We will address the comparison of the FMM classification with others derived using alternative distances, such as [47] , and, reproducible methods such as [48] . An extensive review of cluster multi-channel spike recordings can be found in [49] .…”

Robust Online Multiband Drift Estimation in Electrophysiology Data

“…Finally, a true test of this approach is how it can handle different types of probes, probe designs, and layouts, both Neuropixels and other silicon probes, and how this can alter spike sorting [1, 2, 20]. To this end, we were able to apply our method to the staggered Neuropixels 1.0 version (the two human data sets) and the Neuropixels 2.0 probe (the mouse data set).…”

Spike sorting algorithms and their efficient hardware implementation: a comprehensive survey

“…While each of these alternatives have been proven suitable under certain conditions, it is commonly agreed that spike sorting still offers the highest degree of freedom and accuracy of decoding user intentions in a wide range of settings [26,160]. However, whether the improved accuracy is worthy of the increased hardware complexity depends on the While some applications are able to primarily decode information from multi-neuron activities and can circumvent the need for spike sorting [161], the majority of neuroscience research that investigates the neuronal information encoding [162][163][164], single neuron responses [7] and neuropathophysiology [165,166] still heavily rely on the identification of single unit spikes.…”