To increase the quality of neurophysiological recordings, it seems to be a good idea to improve the biocompatibility of the recording tool, which means that its physical and chemical properties should approximate the ones of the intact neural tissue. One step forward could be an electrode coating or insulation with a boron-doped diamond material (Klempír et al., 2020) or polyethylene glycol (PEG) 4000 for increasing the rigidity of ultra-flexible probes (Guan et al., 2019; Vasileva and Bondar, 2021). Another promising material that unites strength and flexibility on a neural probe scale is carbon fiber, with increasing attentiveness in neurophysiological studies (Cetinkaya et al., 2018). Another procedure that decreases damage during implantation is the slow insertion of the recording devices. For even better results, administration of certain steroidal anti-inflammatory drugs or vasoconstrictive pharmakons, dura mater preparation and blood vessel-sparing penetration may be attempted (Fiáth et al., 2019a; He et al., 2020). Shank volume reduction is also proportional with tissue damage reduction (Musk, 2019). Nevertheless, elasticity has its own drawbacks when surgical procedures are considered; therefore, stiffening supports or even implantation shuttles can be of benefit (Wang et al., 2021). The latter solution would be of key importance when implantation targets dictate a high precision or vascular structures should be exceptionally spared (Fiani et al., 2021).

There has been serious aspirations to decrease electrode impedance by applying special coatings to recording probes, for instance gold plating and polyethylene glycol additives (Kuperstein, 2021), poly (3,4-ethylenedioxythiophene) (PEDOT) (Saunier et al., 2020), or polystyrene sulfonate (Neto et al., 2018). However, it turned out that impedance control is of less importance when conceptualizing an electrode (Neto et al., 2018). Furthermore, impedance can be diminished by simply growing the surface of contacts, possibly entailing signal dissipation and decrease in amplitude (Camuñas-Mesa and Quiroga, 2013). Another more relevant aspect is that when recording potentials of interest, one should be concerned about the reference either theoretically set at zero potential or positioned far enough to be considered at least uncorrelated from recording probes (Jurczynski et al., 2021). Electrode geometry or channel density can also raise non-trivial surgical questions (Rivnay et al., 2017) but more importantly influence the quality of recordings, namely, recording sites located closer to the border of rigid silicon shanks are prone to s with a higher signal-to-noise ratio (Fiáth et al., 2021). Implementation of wider shanks has its two-faced features, too: the more volume covered and neural activity observed by the electrode, the more significant tissue scarring will be (Tóth et al., 2021).

Progress in electrode fabrication has not only yielded to multiply the amount of recording sites at a fast pace (Kim et al., 2018) but also finds the most suitable means for any electrophysiological experiment. Take for example 3D self-rolled biosensor arrays that are meant to collect data from three-dimensional cortical spheroid cultures (Kalmykov et al., 2021), with their high-density channel profile, Neuropixels probes are ideal for functional connectivity studies (Wang et al., 2021). In the following paragraphs, some of the most common sensor types used for electrophysiological recordings are presented.

The plethora of neural sensors does not entitle us to choose the recording parameters further discussed in a rather oblivious manner; on the contrary, we should strive to precisely select our targets within brain structures and their specific physiological conditions for data acquisition, as quality of spike sorting does heavily lean on this step (Hildebrandt et al., 2017). Considering technological factors such as sampling frequency (Irwin et al., 2016), referencing procedures (Jurczynski et al., 2021) and recording data that might be subdivided later (Hassan et al., 2021) are all influencing quality and computational cost. We should also take into account that not every channel would provide the highest quality signal possible, since electrodes detached from their amplifier may broadcast their data intermittently or simply get distorted by noise. For this case, real-time rejection of corrupted channels would be favorable (Swindale and Spacek, 2016; Li et al., 2020a).

Prior to action potential detection, it is worth considering a filtering stage, as lower frequency local field potentials, mostly defined as frequencies below 300–500 Hz, may encumber further analyses (Issar et al., 2020). By this step, the quality of spikes should also enhance; hence, filters behave as balancing factors between incorrectly detected or discarded events even without previous thresholding (Zhang and Constandinou, 2021b). Take for example plain bandpass filtering (e.g., causal infinite impulse response filters), with the advantage of amplitude threshold detection: as feasible as it is, one should also deal with avoiding secondary phase distortion (Schaffer et al., 2021). Another yet computationally expensive non-linear filtering option is wavelet denoising, and the Haar mother wavelet is especially useful when background noise is unlike Gaussian distribution (Barabino et al., 2017; Baldazzi et al., 2020; Pakman et al., 2020). Statistical filtering is based on certain calculated parameters, like average absolute values or standard deviation of sample waveforms (Toosi et al., 2020), while reverse filtering ensures noise diminution by waveform encoding and restoration (Mizuhiki et al., 2020). If abrupt changes in particular data are suspected, particle filtering may confidently detect them, along with accepting the burden of greedy computational needs (Hu et al., 2018). Artifacts occur not just because of imperfect signal filtering but also human-induced signs like stimulation artifacts may contaminate data, and as these objects are highly structured artifacts, statistical filtering may circumvent this source of bias (Mena et al., 2017; Toosi et al., 2020). Despite most algorithms striding to suppress noise, some of them suggest highly contaminated snippet exclusion (Evangelou, 2020). At first sight paradoxically, introducing certain artifacts in the pre-emphasis (Ravikumar, 2021) with an optimum flicker noise intensity called stochastic resonance, signal detection rates significantly improve (Güngör and Töreyin, 2020; Güngör et al., 2021).

As it can be seen, filtering and spike detection are the lead-in operations in spike sorting; therefore, the quality of feature extraction and clustering is greatly impacted by detection algorithm performance, but even if data have been vigorously curated, spotting spike candidates remains a challenge (Okkesim et al., 2021). Filters may be an excellent support for threshold crossing event detection algorithms (Yang et al., 2017; Saggese et al., 2021), although more complicated methods, such as correlation-based detection, wavelet decomposition (Gao et al., 2018), Bayesian shrinkage methods (Sousa et al., 2021), and Teager or smoothed non-linear energy operators may also profit from them (Pagin and Ortmanns, 2017; Tambaro et al., 2020). Once noise level is estimated, amplitude threshold value can be set to a proper value (Barabino et al., 2017), although dynamic changes in noise variance are principally neglected (Toosi et al., 2020), but where is the optimum for picking a threshold? By employing a three-to-five standard deviation threshold, most authors agree that spike prominence is correctly estimated (Laboy-Juárez et al., 2019), while others focus on loss minimization and push threshold values lower (Bigelow and Malone, 2021; Chou et al., 2021). Instead of declaring signal standard deviation as the event detection threshold, it may be reasonable to surge robustness against alternating firing rates and calculate with median values (by recognizing that high amplitudes or spiking activity could only represent a small fraction of the recorded data) (Pregowska et al., 2019). Therefore, an automatically set threshold value could be determined as follows (Quian and Nadasdy, 2004):

where x stands for the filtered signal. Evaluated at 34, the approximation constant is the inverse of the standard normal distribution function, and threshold may be from a two- to four-fold value of σ_n (Quian and Nadasdy, 2004).

Besides threshold crossing, plenty of algorithms enable action potential detection. Smoothed or common non-linear energy operators may be capable of sub-millisecond on-chip spike detection (Malik et al., 2016; Schaffer et al., 2017; Tambaro et al., 2021). Signal-to-noise ratio can be further augmented by amplitude-slope operators (Zhang and Constandinou, 2021b). By applying a Teager energy operator-detector on data, even higher noise levels are well-tolerated; thus, filtering stages may be skipped (Lieb et al., 2017). Another noise-resilient approach represents fractal analysis of neural recordings, and after concluding that segments containing spikes have inferior dimensionality compared to noise, spike detection can be achieved (Salmasi et al., 2016). As it can be candidly imagined, an action potential and its propagation in an extracellular space would not let the entropy content of the temporal dimension unchanged, so calculating it with a sliding window method proved to detect spikes with greater specificity (Farashi, 2018). Combined methods that filter and set threshold parallelly, with adjustable weights depending on the source, are signal-to-noise ratio optimal filters and proposed to reduce computational complexity and upgrade discriminating capability (Wouters and Kloosterman, 2019). As a next chapter in filtering and detection paradigms, neural networks with barely one hidden layer can fulfill the tasks of preprocessing and event detection (Issar et al., 2020).

After successful data filtering and detection of action potentials, spike characteristics should also be explored and mapped. Before the very solution, which is feature extraction, it is reasonable to line up spikes in a way that may facilitate further processing and eventual visualization. This can be rendered by “binning” all spikes into a fixed length window, and then aligning them such that each spike has its temporal reference point, for instance maximum value or slope (Metcalfe et al., 2017; Valencia and Alimohammad, 2021). This method is uncomplicated and vital when opting for clustering alternatives but may fail when noise corruption is elevated (Valencia and Alimohammad, 2019). Such issues may be circumvented by upsampling data and, therefore, performing super-resolution alignment (Lee et al., 2017).

The main pillar for accurate signal decoding is inarguably finding distinctive features in spikes that practically reveal their source. This gain of waveform information is called feature extraction, where only the most critical elements, the so-called principal components, are retained for further assessment (Ravikumar, 2021), which means that later, dimensionality reduction also takes place (Mahallati et al., 2019). For maximizing spike sorting accuracy, it is reasonable to choose principal components wisely, preferably ones that are noise-independent (Soleymankhani and Shalchyan, 2021) and are distinctly discriminative (Lefebvre et al., 2016; Zamani et al., 2020b) but cheap at implementation (Zamani et al., 2020a); by this means, we can map neural data in an informative but lower dimensional space. There is also a difference between first (waveform amplitude) and second (slope of the waveform) principal components (Navratilova et al., 2016). Principal component analysis (PCA), as one of the most popular dimensionality reduction methods (Salmasi et al., 2016; Allen et al., 2018), constructs a matrix of the largest variation-containing orthogonal basis vectors in the feature space (Chen et al., 2021), but extensive computations and storage requirements are inevitable (Regalia et al., 2016; Yang et al., 2017).

The must for unsupervised analysis had urged to think forward PCA. As a result, independent component analysis (ICA) has been created, which, similarly to PCA, has also benefited from redundancy reduction, improved signal to noise ratio, and as a final implementation, fastICA version reduced computation time, therefore turning the initial method into a suitable option for high density channel recordings (Leibig et al., 2016). While appropriate for high-density MEAs, ICA presumes that the set of sources does not outweigh the number of recording channels, and consequently fails when tetrode or low-density neural recordings are analyzed (Buccino et al., 2018). ICA generates both temporal and spatial redundancy, an advantage that can be brilliantly exploited by deep learning: convolutive ICA methods, therefore, are ready to extract features and cluster them in an unsupervised fashion (Leibig et al., 2016).

There is another dissimilar branch of methods that prioritizes cutting back on hardware complexity and template matching (Laboy-Juárez et al., 2019) that is an increasingly popular alternative for clustering. Discrete derivatives (Zamani et al., 2018) or optimal wavelet transforms (Yang and Mason, 2017; Soleymankhani and Shalchyan, 2021), which are sub-band selective, can stand for filtering as well (Soleymankhani and Shalchyan, 2021), whereas zero crossing features (Oh et al., 2017) or first and second derivative spike features (Caro-Martín et al., 2018) are methods that can tackle this condition. These methods are concentrated on global features gripping waveform morphology similarities of action potentials, but local feature extraction, e.g., Laplacian eigenmaps, could constitute another strategy as well (Chah et al., 2011; Huang et al., 2021). Regardless of the choice of feature extraction algorithms, by the end of this step, a well-represented feature space should be received, mapping each spike snippet as part of a highly distinguished and densely populated area (Chung et al., 2017).

The practice of categorizing spikes or their calculated features in such a way that their source neurons would be identical holds the name of clustering (Knieling et al., 2017). All the algorithms and technologies presented so far converge toward clustering, since the goal of decoding extracellular action potentials is acquired by this step. The ideal clustering algorithm runs real-time, implements sequential processing, it is fully unsupervised, but preferably as uncomplicated as clustering and parallel operations may be carried out on the recording device (Wood et al., 2004; Li et al., 2019; Toosi et al., 2020). For simplicity, clustering algorithms may be arranged into model- or non-model-based categories, admitting that even within these groups, algorithms highly differ from each other (Figure 2). This section intends to outline major clustering paradigm strategies without the ambition to compare all of them in terms of performance, execution speed, and other properties.

Model-based or simply probabilistic approaches lean on a source-dependent spike probability distribution provided by a generative model: Bayesian methods, expectation maximization procedures, or maximum likelihood estimations are typical instances of this class. These tactics are unusually resilient to noise associations, and over and above cluster visualization is facilitated (Mahallati et al., 2019). Modeling a mixture of drifting t-distributions enables differentiating overlapping clusters from heavy tails (Shan et al., 2017), while hidden Markov models have been successfully utilized during joint detection and sorting analyses (Li et al., 2019); however, the foremost strain of computational requirements is faintly resolved. The threat of cluster overseparation is also considerable, especially when non-Gaussian clusters, in fact, are assumed to represent Gaussian distribution (Keshtkaran and Yang, 2017; Rezaei et al., 2021).

Non-model-based methods, on the contrary, endeavor on classification tasks (Shan et al., 2017). With almost historical relevance, manual clustering is the most expository approach where obvious parameters like spike amplitude, duration, and channel location are applied as model substitutes (Sun et al., 2021). These methods have been gradually replaced by minimally supervised or supervision-free practices, one of them being k-means clustering, from the partitional subclass. Today, k-means dominates spike sorting protocols because of its lack of sophistication (Dallal et al., 2016; Fournier et al., 2016; Lefebvre et al., 2016; Park et al., 2020; Rácz et al., 2020), along with hierarchical techniques or graph-based, fuzzy logic, and density-, grid- and learning-based methods (Zhang et al., 2018; Veerabhadrappa et al., 2020). Hierarchical solutions are mainly represented by Euclidean distance-employing algorithms (Knieling et al., 2017), which are the base of optimal filter estimation methods, too (Hassan et al., 2020). Graph-based clustering has nearest neighbor interactions at the center, but spectral clustering (Huang et al., 2021) or super-paramagnetic clustering in the well-known wave_clus algorithm does also subscribe to this ground (Quian and Nadasdy, 2004). Fuzzy-C-means logic considers each action potential as a member of every cluster that has been delineated, and only their affinity degree makes decoding possible (Regalia et al., 2016). Density-based algorithms are the most analogous approaches with human clustering strategy, since they focus on agglomerated regions and their low-density belts in the feature space (Chung et al., 2017; Hilgen et al., 2017; Hennig et al., 2019). Learning-based clustering incorporates various means in the service of spike sorting, beginning from single-layer perceptrons to state-of-the-art spiking neural networks (Veerabhadrappa et al., 2020).

Making up our mind for a certain clustering approach does not automatically discard correction procedures or even melting it with another algorithm. The minor corrections imply Laplacian eigenmaps, which boost k-means clustering accuracy (Chah et al., 2011) or subset clustering together with pre-whitening for parameter-free spike clustering (Diggelmann et al., 2018). Consensus and ensemble clustering uses the variability of distinct clustering algorithms (Fournier et al., 2016; Vitale et al., 2019; Zhu et al., 2020); similar contracted measures are Euclidean distance of scatter-plotted features (Berjis and Al-sulaifanie, 2020), k-means clustering combined with mean-shift strategy [with the advantage of calculating the number of optimal clusters and analyzing similarity degrees (Negri et al., 2020)], and hierarchical agglomerative clustering (Cleaver-Stigum, 2021).

Spikes may even be decoded with a recording device, and power consumption; data quantity will also benefit from it (Liu et al., 2018). Contemporary tools to fulfill this task are mainly based on field-programmable gate arrays or application-specific integrated circuits (Barnett et al., 2016). Nevertheless, microcontroller units (Schiavone et al., 2020) and system-on-a chip devices (Liu et al., 2017) are increasingly popular. These chips, however, must be trained in preceding spike sorting procedure for successive fine tuning and accurate execution of functions (Zeinolabedin et al., 2016; Saeed et al., 2017). This approach is also vital in the field of neuromorphic computation, ignited by very large-scale integration technologies (Mukhopadhyay et al., 2018); there has recently been an implementation for 65,536 simultaneously recording and stimulating electrodes (Tsai et al., 2017). Similarly, extensive neural recordings are preferentially processed in batches and then subdivided into bins given the resistive state that calls for proper noise estimation (Gupta et al., 2019).

Clustering itself can be any algorithm from those introduced in the general clustering section, but it is worth considering the computational bottleneck of recording front ends. As it could be seen, most non-model based clustering algorithms are intended to capture specific geometric features; therefore, finding the most prominent ones could also cut back on the number of features under analysis (Shaeri and Sodagar, 2020). Event-trace, template-value differences add a temporal dimension to the template matching procedure, so they can further reduce the number of comparison units (Haessig et al., 2020). Learning-based methods may also control this circumstance at a superior level; hence, it is sufficient to upload a ready-to-use, modest as possible pretrained artificial neural network (Valencia et al., 2019). In this subject, hardware-embedded spiking neural networks are the greatest novelty, mostly owing to their feasible adaptation to data recorded on the fly (Werner et al., 2016).

The ever-increasing need for spike sorting has led to a wide range of open-source electrophysiological platforms, frameworks, and software (Pachitariu et al., 2016). Any related project is either aimed to bring spike sorting closer to a user who has little knowledge of the procedure or provide a space for methodology comparison and dataset generation. It is also required to support a wide range of data formats, as well as oscillating quality and length of the recordings (Swindale et al., 2017). Here, we briefly present novel toolboxes from the past 5 years that can be applied straightforward even by non-expert users.

For data acquisition, the Parallel Ultra-Low-Power (PULP) platform merges the process of data acquisition and single unit detection under a single benchwork (Schiavone et al., 2020). Another software, Neural Parallel Engine, is useful when the execution speed of spike sorting algorithms is crucial, since it enables highly parallelized computational process through graphical processing units (Tam and Yang, 2018). Neurophysiological data can be curated and further analyzed with Phy², a graphical user interface operating with Python. Another manual curation-supporting graphical user interface is based on t-student stochastic neighbor embedding (Dimitriadis et al., 2018b).

Python frameworks have also been created for complete spike sorting procedures such as OpenElectrophy (Rosenberg and Horn, 2016), herding_spikes (Muthmann et al., 2015), NeoAnalysis (Zhang et al., 2017), tridesclous³, and spyke⁴. All of them support multichannel architectures. At the center of MountainSort, a density-based clustering approach stands, namely, ISO-SPLIT; this suite is also open-source (Chung et al., 2017).

SpikeInterface is a framework that not only offers algorithms for spike sorting, but most recent sorters can be used interchangeably (Buccino et al., 2020). Similarly, Spikeforest is also available for a wide range of sorting approaches; moreover, their comparison has never been easier because of its intuitive graphical user interface (Magland et al., 2020). Spikeforest, which may be embedded into the SpikeInterface environment, evaluates algorithms automatically and systematically based on some of the most well-known datasets supplied with ground truth. The Spike-Sorting Evaluation Initiative and the 1st INCF Workshop on Validation of Analysis Methods (Denker et al., 2012) have pushed the spike sorting community toward sharing essential data for algorithm evaluation, but cloud computing as a helping feature is also considered (Mahmud and Vassanelli, 2019).

When searching for unified computational for a more specific use, P-sort is a unique pipeline and software, destined to sort cerebellar single unit activities (Sedaghat-Nejad et al., 2021). CellExplorer suits for single-neuron characterization and signal visualization are for those who are confident with MATLAB (Petersen et al., 2020), whereas Big Neuronal Data Format (BNDF) emphasizes large-scale data processing and reducing overall runtime (Hadianpour et al., 2021). Combinato, on the other hand, is written in C/C++ and enables dealing with long-term, noisy recordings mostly in an unsupervised fashion (Knieling et al., 2016). For algorithm validation, SHYBRID is a surrogate platform when ground truth information is expected, offering hybrid data as an evaluation tool (Wouters et al., 2021).

Another road to unburdening spike sorting is to simulate datasets in which algorithms can be trained, validated, and tested. ViSAPy (Hagen et al., 2015) and MEArec (Buccino and Einevoll, 2021) are Python-based, whereas Neurocube (Camuñas-Mesa and Quiroga, 2013), Neural Benchmark Simulator (Mondragón-González and Burguière, 2017), and SHYBRID (Wouters et al., 2021) are MATLAB-implemented frameworks (Figure 5).

By increasing channel counts and covered brain areas, we get an insight into more neurons' activities in parallel. This means that statistical models based on such recordings would also hold a promise for superior neural population activity interpretation (Hurwitz et al., 2021). Nevertheless, computational costs escalate almost exponentially with channel numbers, and the number of wires that send signals forward would also grow, except for methods employing controllable switches to attach recording sites with single wires (Lee et al., 2021). Increasing channel density and adopting a divide-and-conquer processing strategy (Chen et al., 2021) allows for effortless detection of duplicated or overlapping spikes (Larionov et al., 2019; Chou et al., 2021; Dehnen et al., 2021) and provides more detailed spatial information on action potential sources (Rácz et al., 2020). Meanwhile, by increasing the number of spikes sorted out, false positive or negative detections become an imaginable source of errors: a small quantity of mistakenly identified spikes perturb firing rate or interspike interval values (Chiarion and Mesin, 2021).

For a short time, almost every detected spike waveform keeps its original shape, which is the pseudo-stationary stage of the recording, and any occurring signal variability is mainly given by Gaussian-distributed noise (Yu et al., 2011). On the other hand, in vivo and sometimes in vitro setups suffer from small changes in electrode positions, even from the beginning of recordings, when, e.g., perceptible discrete brain sample dislocations that may impact initially recognized waveforms to an extent that clusters may split or even intercept each other and form new but misclassified groups of action potentials (Gong et al., 2016; Harris et al., 2016; Chaure et al., 2018). The most plausible solution for probe drift is to track and update primordial neuron templates (Lee et al., 2017) or handle data as independent batches, and then ultimately merge alike-looking clusters, but considering spikes with a mixture of drifting t-distribution character can also eliminate drift-suggesting heavier tails of clusters on data representation (Shan et al., 2017). Spike amplitude change may also be estimated with recursive least-squares, but its utility is mostly demonstrated by low-count channel recordings (Davey et al., 2020).

Overlapping spikes frequently cause a problem for spike sorting algorithms: when different neurons fire in such a restrained time window that their waveforms overlay (Rey et al., 2015), the features of their subcomponents extracted for clustering cannot be applied. It is reasonable and right to suspect that the extent of overlap defines the trouble of accurate classification; yet quite paradoxically, evidence shows that firing rate and spike-train correlation levels do not infer with algorithm performance change (Garcia et al., 2021). If data suggest that these events, by incidence, are sufficiently low, simply censoring spikes with double peaks may overcome the problem, and for detections that represent only a mild overlap, deconvolution can be employed (Li et al., 2020b). Others emphasize the ubiquity of overlapping spikes especially when recording with high-density channel count probes and urge for specific algorithms committed to defeat misclustering. A straightforward approach suggests resolving the overlap in the feature space by treating problematic spikes' feature vectors as linear superpositions (Wouters et al., 2020), enhanced by one-hot encoding (Wouters, 2020), or quite the contrary, by fusing features in behalf of dimension number and missing information reduction (Li et al., 2018), while others suggest adding an extra step of combining pair-wise action potential waveform templates at various time shifts for refining sorting accuracy as much as 30% (Mokri et al., 2017). Additional alternative strategies are biogeography-based optimization (Chiarion and Mesin, 2021), using blind source separation methods (Leibig et al., 2016) or close examination of each spike cluster's center, which is equally close to another two midpoints (Wouters and Kloosterman, 2021), by automated template merging (Chen et al., 2021). Sparse representation or compressive sensing of neural data performs peculiarly well when spike waveforms are alike; therefore, overlapping spikes can be resolved by this means (Wu et al., 2018a; Huang et al., 2020). Wavelet Packets Decomposition and Mutual Information (WM sorting) is a clustering algorithm specially designed for overlapping spikes outperforming most of the methods presented here; nevertheless, its computational intensity generates doubts about real-time applications (Huang et al., 2019).

Noticing in a recording channel a set of action potentials, with short, ~3–5-ms interspike intervals, mostly similar in shape but decreasing in amplitude, conventionally means there is a neural burst or “complex spike” (Rey et al., 2015; Evangelou, 2020). Emitted usually by pyramidal cells, in the standard signal generator circuit and by interneurons in deeper structures, the latter ones act by suppression (Gainutdinov, 2021). But why do they constitute a problem when spike sorting is at stake? First, decreasing amplitudes may hamper detection; moreover, this decrescendo may be the source of falsely created separate clusters; second, they can mimic other problem sources: complex waveforms may originate from overlapping spikes (Rácz et al., 2020), and diminishing may be due to electrode drifting. Bursting neurons can be handled by assigning them a burst index (Sun et al., 2021) or a special spike label (Kapucu et al., 2016), or by simply decomposing a spike train and its successors into a parent wave (Chung et al., 2017).

The ability to achieve recordings from freely moving animals with a promise of proper neural decoding over months, hopefully with the same device, is the holy grail of spike sorting (Shan et al., 2017). Long-term recordings, therefore, comprise all arising challenges that could be met throughout spike sorting, but the main problem is represented by unit instability, when new-found spike waveforms replace former ones; hence, most template matching strategies fail (Toosi et al., 2020). Unit firing rate variations, neural plasticity, or loss of recorded neurons in the long run may also influence sorting accuracy (Okun et al., 2016; Xiao et al., 2019; Vasileva and Bondar, 2021), although large-scale, multisite platforms may reconcile even these obstacles (Chung et al., 2020; Vasileva and Bondar, 2021). In the long run, neural sensors may determine leptomeningeal proliferation and fibrosis or even foreign body reaction, all these possibly leading to device encapsulation, further deteriorating recording quality (Szymanski et al., 2021).

It has been widely debated why generally, a recorded neural activity is sparser compared to anatomy-based expectations (Ahmadpour et al., 2019), However, research may suggest that the “dark neuron” problem could be accounted to neural sensors, since their de facto electrode sensibility is usually lower than their anticipated value; therefore others caution on the non-negligible nature of subthreshold signaling and urge for pushing further technical limitations (Shmoel et al., 2016).

Validation of spike sorting performance is almost inconceivable without ground truth. This concept is based on the a priori knowledge of an action potential source, more specifically, sensing when and which neuron in the neighborhood was active (Neto et al., 2016). Ground truth data generation involves human interaction to a certain extent, turning it to a supervised process (Wouters et al., 2021).

What can be done when “ground truthing” is not an option? The most straightforward and popular method is to generate synthetic data and, with these, access ground truth (Buccino and Einevoll, 2021). Hybrid ground truth is accessible by spatially oversampled data acquisition or synthesis (Pachitariu et al., 2016; Scholvin et al., 2016; Wouters et al., 2021), although enriching data with formerly isolated or artificial spikes is also an option, coined hybrid method (Yger et al., 2018). Some classical approaches include manual labeling or simultaneous recording of intra- and extracellular activity, with the latter providing ground truth regarding a single neuron (Fournier et al., 2016; Diggelmann et al., 2018; Abbott et al., 2020). Recently, an automatic patch clamp technique in vivo lightened the workload, but human intervention cannot yet be erased (Kodandaramaiah et al., 2016; Allen et al., 2018). Simultaneous juxta-and extracellular recordings are also applied instead of in vivo patch clamping (Hunt et al., 2019; Magland et al., 2020; Urai et al., 2021). An appealing alternative for compensation of ground truth is to follow over the long run extracellular action potential propagation alongside single axonal arbors and, therefore, assessing their correlation with relatively stable extracellular action potentials; empirical ground truth can be obtained (Tovar et al., 2018). A last compromise for lacking ground truth can stand in the application of the internal criteria indices presented previously (Zhang and Constandinou, 2021a). This choice may be shadowed by certain tacit assumptions of internal criteria applications, such as the Gaussian nature of noise distribution.

Spike sorting is not an art for art's sake protocol, since its applications are visibly boosting contemporary neuroscience: it has become essential in extraction of individual neuronal activities from multi-electrode data, since each electrode reports the collective activity of multiple nearby neurons. But why is carrying out this task cardinal? The fact that neighboring neurons are not necessarily the nearest in connections, i.e., are activated by different pathways or stimuli (Rey et al., 2015) mainly owing to information processing and energy optimization through structural solutions (Pregowska et al., 2019) calls the promise of neural decoding. This concept, although not equal with spike sorting, relies heavily on it and undertakes the risk of bias and wrong intensity estimation generated by spike sorting (Shibue and Komaki, 2017). Spike sorting also incites statistical analyses, involving correlogram analysis, inter-spike intervals, or spike rates (Veerabhadrappa et al., 2020).

When we talk about spike sorting we classically imply manual sorting alongside automated methods (Dai et al., 2019). Given its time-consuming feature and potential for subjective bias (Febinger et al., 2018), manual sorting has been mostly overtaken by history; consequently, there is a sore need for fully automated algorithms (Chah et al., 2011). High-density microelectrode arrays foster progressing classification accuracies; however, computational capacity should also fall in line with recording performance (Chen et al., 2021). Several authors, most of them stressing the expenses of calculation, argue against the pertinence of spike sorting. As it may be expected, alternatives for spike sorting all have their strengths and limitations. According to those who subscribe to rather overtake spike sorting, when the sum (Li and Li, 2017) or moments (Sonia et al., 2014) of waveform features are calculated, spike sorting can be omitted for motor imagery task neural decoding; however, even these methods fall short of real-time reconstructions. Similarly, frequency spectrum maps together with temporal energy heatmaps can predict imaginary finger movements but in a well-defined force amplitude interval (Xu et al., 2020).

Spike sorting indeed does not have to be compulsory when population activity is targeted (Trautmann et al., 2019); therefore brain-computer interface systems that engage in multi-unit activity may settle for less complex preprocessing techniques. Such a method is “binning,” by which firing rates are estimated in a fixed time window (Ahmadi et al., 2020), and with marked point models (Yousefi et al., 2020) or interspike interval histograms combined with power spectrum density estimation, complete firing patterns can be investigated (Guo et al., 2020): these methods are advantageous when tuning curves of single neurons may be distributed bimodally, such as in the case of murine head direction cells (Liu and Lengyel, 2021). Whenever applying a clustering-free method, one should keep in mind that its goodness-of-fit evaluation may differ from mainstream clustering algorithms (Tao et al., 2018). Nonetheless, at the level of individual cells, properties cannot be completely evaluated with spike sorting algorithms (Rossi-Pool and Romo, 2019). To conclude, an eventual conflict between spike sorting methods or data should not discourage us from conducting spike sorting whenever there is a clear indication of employing it.

As the demand for spike sorting is clear-cut, we considered further stressing the relevance of these sets of algorithms by presenting research fields that greatly benefit from spike sorting. Spike sorting is mostly regarded as the essential step toward functional brain-machine interfaces and micro-electronic neural bridges (Huang et al., 2016), but its relevance is substantial in epilepsy research (Neumann et al., 2017; Richner et al., 2019) or the study of sleep (Kozák et al., 2020; Matsumoto, 2020). Besides common-term primary motor cortex single unit activity, clustering in areas where visual coding or multisensory integration takes place is an intuitive approach in vision and adaptive behavioral studies (Reber et al., 2019; Mizuhiki et al., 2020; Steinmetz, 2020). Cerebellar spikes should also be detected and clustered; nevertheless, the latter task is rendered more difficult by the intricate morphology of Purkinje cells (Markanday et al., 2020). Complex spikes can also be met in the thalamus, and spike sorting is just a way to learn about its electrophysiological properties (Pastor and Vega-Zelaya, 2020). In the subthalamic nucleus, clustering of spike trains may help to understand the pathophysiology of movement disorders (Kaku et al., 2019; Sukiban et al., 2019), while the basolateral amygdala or the hippocampus can offer ideas about general neural interactions (Hojjatinia et al., 2020; Oghazian et al., 2020). Correlations calculated after vigorous spike sorting in multichannel data gave rise to a promising neural encoding capacity hypothesis (Isbister et al., 2021) and neural populational activity dynamics (Theilman et al., 2021).

Furthermore, spike sorting may be just as pivotal outside of the brain. Remaining at the level of the central nervous system, superficial dorsal horn spinal cord neurons reveal much about spinal plasticity, and sorting their activities may ensure isolation of relevant populations (Smith et al., 2020). For peripheral nervous system recordings, discrimination and identification of APs cannot lack spike sorting either (Metcalfe et al., 2021). Spike sorting is required to isolate retinal ganglion cells based on their multiunit activity recordings (Tsai et al., 2017; Pérez-Ortega et al., 2021) and identify their electrical responses (Li et al., 2021), but non-neural tissues can also benefit from it by applying spike sorting algorithms, i.e., on pancreatic biosignals (Iniguez-Lomeli et al., 2021).

Spike sorting has its own advantages during in vitro experiments, too. Cerebral organoids, also known as Minibrains, and spike sorting together enable studying neuro/gliogenesis and connectivity (Govindan et al., 2021), while spike sorting in its intracellular variations can elucidate synaptic plasticity mechanisms (Ghanbari et al., 2017). Recently, spike sorting proved to be inescapable when performing optogenetic stimulation on MEA-supported brain slices (Sacher et al., 2021).

Up to this point, we presented spike sorting as a complete procedure that seeks to assign a signal to a particular source, but regarding it as a piece of puzzle toward another problem solution is also valid. Spike sorting can be embedded in synaptic connectivity estimation algorithms, thus helping construct neuronal circuit diagrams (Endo et al., 2021). By combining spike sorting with phasic unit selection, we can recognize firing patterns in structures that have timekeeping properties (Chrobok et al., 2021).

Our study intended to outline some of the most relevant issues that shape current spike sorting trends. Despite the endless attempts and various strategies, obvious spike sorting standards and algorithm comparison methods are still deficient, but any effort toward unified spike sorting frameworks and accuracy quantification metrics should be cheered (Rey et al., 2015; Smith et al., 2020). In spite of closely focusing on a set of algorithms that ascribe a well-defined signal to its supposed emitter, we should not miss the bigger picture either and, therefore, concentrate on the non-independent nature of neural activities (Urai et al., 2021). In the search for the gold standard algorithm, we should also leave room for neural network-based or custom-tailored solutions too, without which we cannot excel when miscellaneous temporal or spatial particularities are present (Vu et al., 2018; Sedaghat-Nejad et al., 2021). The authors of this article believe that fostering the concept of incremental learning in spike detection and clustering methods could offer the panacea for most difficulties arising during long-term recording analysis, since adaptation to alternating circumstances would not demand repeated training on previous samples but focus on the gradually growing set of data without having to worry for memory restrictions.

Another aspect that opens new perspectives is the myriad of neural sensors at our fingertips. As it can now be anticipated, CMOS technologies could fulfill the requirements for implantable long-term sensors, as their ultra-low power consumption, neuromorphic design, and, lately, their capability to self-repair should be exploited (Rahiminejad et al., 2021). Even though a great variety of recording devices provides us with data of ever-improving signal-to-noise ratios and of diminishing invasiveness, there are fundamental questions about extracellular action potentials that are, so far, unanswered. In particular, a probe's interference with the adjacent neural tissue, eventual chance of bias for certain neuron subclasses, cross-compliance between neuron types, and extracellular signatures are hotly debated topics (Neto et al., 2016). None of these subjects are self-standing, since contemplating about the limitations of recording facilities could also possibly bring about the advent of better-built BMI systems.