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Objective. Recent advancements in electrode designs and micro-fabricationtechnology has allowed existence of microelectrode arrays with hundreds ofchannels for single-cell recordings. In such electrophysiological recordings,each implanted micro-electrode can record the activities of more than oneneuron in its vicinity. Recording the activities of multiple neurons may alsobe referred to as multiple unit activity. However, for any further analysis,the main goal is to isolate the activity of each recorded neuron and thuscalled single-unit activity. This process may also be referred to as spikesorting or spike classification. Recent approaches to extract SUA are timeconsuming, mainly due to the requirement of human intervention at variousstages of spike sorting pipeline. Lack of standardization is another drawbackof the current available approaches. Therefore, in this study we proposed astandard spike sorter: SpikeDeep-Classifier, a fully automatic spike sortingalgorithm. Approach. We proposed a novel spike sorting pipeline, based on a setof supervised and unsupervised learning algorithms. We used supervised, deeplearning-based algorithms for extracting meaningful channels and removingbackground activities (noise) from the extracted channels. We also showed thatthe process of clustering becomes straight-forward, once the noise/artifact iscompletely removed from the data. Therefore, in the next stage, we applied asimple clustering algorithm (K-mean) with predefined maximum number ofclusters. Lastly, we used a similarity-based criterion to keep distinctclusters and merge similar-looking clusters. Main results. We evaluated ouralgorithm on a dataset collected from two different species (humans andnon-human primates (NHPs)) without any retraining. We also validated ouralgorithm on two publicly available labeled datasets.

SpikeDeep-Classifier: A deep-learning based fully automatic offline spike sorting algorithm