Abstract
Spike Sorting is a challenging problem in Computational Neuroscience because of the complexity of neural data. One of the greatest issues are overlapping clusters. This paper focuses on the feature extraction step in the Spike Sorting pipeline and proposes an adaptation of Principal Component Analysis (PCA) to increase the separability between clusters. This is achieved by weighting the features before applying PCA, taking into consideration the multimodality and the distance between probability distributions. The information extracted from the characteristics of a multimodal distribution is the number of modes (peaks). The distance between the probability distributions is quantified using Jensen-Shannon divergence. The computed information, number of modes and distance, is aggregated into a coefficient representing the weight of the features. The new approach has been validated on a synthetic dataset and shows improvements compared with the state-of-the-art PCA.
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