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

ABSTRACT: 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 templa

SUBMITTER: Kim MK 

PROVIDER: S-EPMC11606139 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature