## 5 Conclusions  
This study presents a significant advancement in spike sorting by strategically combining established techniques with a pioneering application: Self-Organizing Maps (SOMs) for biosignal analysis. SOMs excel in unsupervised learning and dimensionality reduction, revealing the complexities of neural signal analysis from macaque monkey and human pancreatic signals. This multi-faceted endeavor unveils the nuanced effectiveness of SOMs and leverages them to form cohesive neural patterns through hierarchical clustering. Our approach successfully classified the neural data, achieving results comparable to established methods. Building on the foundation of adaptive thresholding, which effectively isolated action potentials while filtering background noise, PCA emerged as a crucial step for dimensionality reduction. This enhanced data interpretability and facilitated the identification of previously misclassified noise spikes in macaque monkey signals. The combined power of PCA and K-means in human pancreatic signals transformed a binary classification into one with three distinct groups. The introduction of SOMs added a novel unsupervised learning dimension. SOMs delineated clusters based on spatial distribution by revealing underlying data topology, offering a refined perspective on neural signal organization. Hierarchical clustering further refined this process, forming cohesive groups based on node proximity. This method effectively captured significant clusters without over-segmentation, achieving results comparable to PCA and K-means, suggesting PCA might be optional for specific analyses.The proposed approach successfully disentangled complex neural patterns, identifying six types of potentials. This robustness, mirrored in the similarity of results to the PCA-augmented K-means method, underscores the efficacy of the methodologies. Future research can explore additional clustering algorithms, feature extraction methods, and more sophisticated noise-filtering techniques. Extending this analysis to various real-world scenarios where other types of biosignals could validate the generalizability of these methods, this will be imperative for future research. Additionally, investigating real-time implementations and integrating these techniques into clinical applications or brain-computer interfaces is essential for improving signal processing and information decoding for real-world applications, thus offering promising avenues for advancing neural science and its practical applications.  
**Author Contributions:** F.J.I.-L.: conceptualization, data curation, formal analysis, investigation, writing--original draft; E.E.F.-O.: data curation, formal analysis, investigation, methodology, resources, supervision; A.M.S.G.-A.: conceptualization, formal analysis, methodology; A.A.G.-G.: conceptualization, funding acquisition, investigation, methodology, resources, validation; H.R.-G.: conceptualization, data curation, formal analysis, funding acquisition, investigation, methodology, project administration, resources, software, supervision, validation, visualization, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by CONAHCYT grant numbers 809220, 809470 and 413813.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Acknowledgments:** We thank the Laboratoire de l'Integration du Materiau au Systeme (IMS), University of Bordeaux, CNRS UMR 5218, Bordeaux INP, Bordeaux, France, in particular, [PERSON] and [PERSON] who provided the signals used in this work.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
Figure A5: (a) Spike shape of the new group detected with K-means and PCA from the detected spikes in the macaque monkey signal. (b) Noise signal with the same amplitude as the spike.