## 1 Introduction  
The realm of neural signal analysis has witnessed remarkable advancements with the emergence of clustering methods that play a crucial role in uncovering intricate patterns within multi-dimensional data [1; 2; 3]. Among these techniques, spike sorting is crucial, facilitating the isolation and classification of individual action potentials from complex recordings [4; 5; 6]. Accurate spike sorting is fundamental for unveiling neural dynamics, understanding network interactions, and deciphering the underlying mechanisms governing neural behavior [7]. However, the extraction of action potentials from biosignals has presented formidable challenges due to the pervasive presence of noise, interference, and the diversity of action potential shapes in biological recordings [8; 9; 10]. In recent years, these main drawbacks have been studied from a computational viewpoint, where two seminal works have been published [11; 12]. In these articles, the most commonly used techniques for biosignal analysis and spike sorting are mentioned, highlighting algorithms such as Wavelets and PCA as feature extraction techniques. Some newer algorithms, such as Support Vector Machines and neural networks, are also mentioned [13; 14]. In the particular case of PCA, three papers mention that this technique has been used to reduce the dimensionality of the data. For clustering, some methods such as Bayesian and hierarchical clustering and Gaussian mixtures are also mentioned as a possibility to analyze this kind of data [15]. The methodologies reviewed in these two papers leverage the synergistic potential of machine learning, in particular, clustering methods and signal processing, to unravel the complexities of action potential detection and classification. Recent articles have introduced novel methods for spike sorting: In [16], they introduced an approach for spike sorting by using a one-dimensional convolutional neural network; their method achieved high accuracy in synthetic data. However, it has not been tested with real acquisitions that have varying noise levels and overlapping spikes. They outperformed methods like WMsorting and deep learning multi-layer perceptron (MLP) models. The study by [17] proposed a deep learning-based technique for spike sorting, termed deep spike detection (DSD), to improve spike detection accuracy. DSD incorporates two convolutional neural networks (CNNs) into the conventional spike sorting pipeline. These CNNs are employed for selecting active neural channels and removing artifacts from the chosen channels, enhancing the overall classification performance. In [18], they present another approach to spike sorting using a deep learning-based autoencoder method. This method aims to improve clustering performance compared to traditional techniques like PCA and ICA. The improvement has been demonstrated using both synthetic and real datasets. Autoencoders are capable of learning underlying features from unlabeled data. Spike sorting involves extracting informative features from spike waveforms, and autoencoders can discover these features in an unsupervised manner, eliminating the need for labeled data, which can be difficult to obtain in spike sorting. Additionally, autoencoders have been shown to be robust to noise, which is a common challenge in spike sorting due to the low signal-to-noise ratio of recordings. In [19], the authors proposed a deep learning network based on a convolutional neural network and sliding window Long Short-Term Memory for spike sorting. LSTM [20] is a recurrent neural network architecture that has recently attracted attention in the study of biosignals due to its ability to process spatiotemporal information. This paper presents important results for low noise and simulated data, which is not the case for real neural recordings. In the same way as reinforcement learning, [PERSON] et al. [21] proposed a method for sorting under multi-class imbalance in two public datasets with real neural recordings.