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.

Keywords: algorithm evaluation; clustering; neural sensors; single unit recordings; spike sorting.

Figure 1

From brain to spike. Gaining, sorting, and employing of spikes begin with the acquisition of neural signals, either from human or animal neural tissues. Microelectrode arrays and micro-assembled probes are two of the most frequently used modalities of neural tissue recordings. These recordings are routinely filtered in order to render them more accessible for the conventional spike sorting procedure. As a result, single unit activities would be recognized and finally organized into clusters based on similar morphology.

Figure 2

Feature classifications of the most widely used clustering algorithms. When choosing the ideal algorithm for a given dataset, it is straightforward to imagine a decision tree and consider which features are essential during the clustering process: supervised or unsupervised, and then the level of accuracy, even with acceptance of enormous computational costs.

Figure 3

(A) Algorithm accuracy vs. the number of units found above the accuracy threshold of 80%. (B) Algorithm accuracy vs. time needed for computation. Algorithms were tested on the Hybrid_Janelia dataset, with a minimum SNR of 10.

Figure 4

Comparison of clustering algorithms based on their accuracy achieved in the wave_clus dataset. We selected novel state-of-the-art algorithms that cannot yet be evaluated through the SpikeForest framework, but their performance in the wave_clus dataset has been made available with their publication. Generally, 16 samples of this dataset are applied during validation and divided into the subgroups “easy1,” “easy2,” “difficult1,” and “difficult2,” which appear in our x axis as “e1,” “e2,” “d1,” and “d2,” respectively. The values after the underscore reveals the noise content of each simulation, i.e., _005 means a 5% noise contamination, whereas _01 10, _015 15, and _02 20% sequentially.

Figure 5

Enlisting signal processing toolboxes based on their most useful functions. “Signal acquisition” box: a wide variety of toolboxes treating difficulties of the recording or signal generation process, most of them compressing or prefiltering data for further steps. “Data curation + data format” box: software indicated for preprocessing previously recorded signals, most of them recommended even in the case of manual spike sorting. The “sorting” box lists the most used and trusted spike sorting algorithms or algorithm collections that can be applied to various datasets. Finally, the “algorithm evaluation” and “algorithm comparison” box offers help when a custom algorithm needs validation or measuring against the algorithms itemized in the “sorting” box.