Developing high-density electrodes for recording large ensembles of neurons provides a unique opportunity for understanding the mechanism of the neuronal circuits. Nevertheless, the change of brain tissue around chronically implanted neural electrodes usually causes spike wave-shape distortion and raises the crucial issue of spike sorting with an unstable structure. The automatic spike sorting algorithms have been developed to extract spikes from these big extracellular data. However, due to the spike wave-shape instability, there have been a lack of robust spike detection procedures and clustering to overcome the spike loss problem. Here, we develop an automatic spike sorting algorithm based on adaptive spike detection and a mixture of skew-t distributions to address these distortions and instabilities. The adaptive detection procedure applies to the detected spikes, consists of multi-point alignment and statistical filtering for removing mistakenly detected spikes. The detected spikes are clustered based on the mixture of skew-t distributions to deal with non-symmetrical clusters and spike loss problems. The proposed algorithm improves the performance of the spike sorting in both terms of precision and recall, over a broad range of signal-to-noise ratios. Furthermore, the proposed algorithm has been validated on different datasets and demonstrates a general solution to precise spike sorting, in vitro and in vivo.

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Developing new techniques of simultaneous recoding using thousand electrodes, make the wide variety of spike waveforms across multiple channels. This problem causes spike loss and raise the crucial issue of spike sorting with unstable clusters. While there exist many automatic spike sorting methods, there has been a lack of studies developing robust and adaptive spike detection algorithm. Here, an adaptive procedure is introduced to improve the detection of spikes in different scenarios. This procedure includes a new algorithm which aligns the spike waveforms at the point of extremums. The other part is statistical filtering, which seeks to remove noises that mistakenly detected as true spike. To deal with non-symmetrical clusters, we proposed a new clustering algorithm based on the mixture of skew-t distributions. The proposed method could overcome the spike loss and skewed cells challenges by offering an improvement over automatic detection, alignment, and clustering of spikes. Investigating the sorted spikes, reveals that proposed adaptive algorithm improves the performance of the spike detection in both terms of precision and recall. The adaptive algorithm has been validated on different datasets and demonstrates a general solution to precise spike sorting, in vitro and in vivo.

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