Unsupervised Spike Detection and Sorting with Wavelets and Superparamagnetic Clustering

TLDR

The study proposes an unsupervised method for detecting and sorting spikes from multiunit recordings, including a novel amplitude‑thresholding approach. The method uses wavelet transforms to extract spike features and superparamagnetic clustering for automatic classification, yielding a fast, assumption‑free algorithm that was benchmarked against conventional techniques on realistic simulated datasets. On simulated datasets mimicking in vivo recordings, the algorithm outperformed conventional spike‑detection and sorting methods.

Abstract

This study introduces a new method for detecting and sorting spikes from multiunit recordings. The method combines the wavelet transform, which localizes distinctive spike features, with superparamagnetic clustering, which allows automatic classification of the data without assumptions such as low variance or gaussian distributions. Moreover, an improved method for setting amplitude thresholds for spike detection is proposed. We describe several criteria for implementation that render the algorithm unsupervised and fast. The algorithm is compared to other conventional methods using several simulated data sets whose characteristics closely resemble those of in vivo recordings. For these data sets, we found that the proposed algorithm outperformed conventional methods.