Exploiting the Self-Organizing Map for Spike Sorting

Spike sorting strongly relies on the exploitation of similarity of features extracted from waveforms of superimposed spikes of neurons. Main issues of such a task are given by overlapping clusters due to imperfect features and cluster imbalance that is inherent to neuronal data, as different types of neurons exhibit various firing rates. Current techniques are still highly affected by these drawbacks. To address these issues, a Self-Organizing Map-based technique is proposed, which is a neural network capable of unsupervised learning from which the unified distance matrix is extracted and employed in an additional postprocessing step that allows the proposed method to substitute both the feature extraction and the clustering steps of the spike sorting pipeline. The performance evaluation encompasses synthetic datasets, which are publicly available for the purpose of benchmarking. These datasets contain a high number of clusters, allowing for comprehensive testing and comparison of the proposed method. The results of the evaluation indicate that the proposed method outperforms other state-of-the-art techniques in terms of spike sorting performance.