Synthetic data-driven overlapped neural spikes sorting: decomposing hidden spikes from overlapping spikes.

Sorting spikes from extracellular recordings, obtained by sensing neuronal activity around an electrode tip, is essential for unravelling the complexities of neural coding and its implications across diverse neuroscientific disciplines. However, the presence of overlapping spikes, originating from neurons firing simultaneously or within a short delay, has been overlooked because of the difficulty in identifying individual neurons due to the lack of ground truth. In this study, we propose a method to identify overlapping spikes in extracellular recordings and to recover hidden spikes by decomposing them. We initially estimate spike waveform templates through a series of steps, including discriminative subspace learning and the isolation forest algorithm. By leveraging these estimated templates, we generate synthetic spikes and train a classifier using their feature components to identify overlapping spikes from observed spike data. The identified overlapping spikes are then decomposed into individual hidden spikes using a particle swarm optimization. Results from the testing of the proposed approach, using the simulation dataset we generated, demonstrated that employing synthetic spikes in the overlapping spike classifier accurately identifies overlapping spikes among the detected ones (the maximum F1 score of 0.88). Additionally, the approach can infer the synchronization between hidden spikes by decomposing the overlapped spikes and reallocating them into distinct clusters. This study advances spike sorting by accurately identifying overlapping spikes, providing a more precise tool for neural activity analysis.