@article {68936,
	title = {Multi-day neuron tracking in high-density electrophysiology recordings using earth mover{\textquoteright}s distance},
	journal = {eLife},
	year = {2024},
	month = {2024-07-10},
	abstract = {<p>Accurate tracking of the same neurons across multiple days is crucial for studying changes in neuronal activity during learning and adaptation. Advances in high-density extracellular electrophysiology recording probes, such as Neuropixels, provide a promising avenue to accomplish this goal. Identifying the same neurons in multiple recordings is, however, complicated by non-rigid movement of the tissue relative to the recording sites (drift) and loss of signal from some neurons. Here, we propose a neuron tracking method that can identify the same cells independent of firing statistics, that are used by most existing methods. Our method is based on between-day non-rigid alignment of spike-sorted clusters. We verified the same cell identity in mice using measured visual receptive fields. This method succeeds on datasets separated from 1 to 47 days, with an 84\% average recovery rate.</p>
},
	doi = {10.7554/eLife.92495.3},
	url = {https://doi.org/10.7554/eLife.92495.3},
	author = {Augustine Xiaoran Yuan and Jennifer Colonell and Anna Lebedeva and Adam Charles and Timothy Harris}
}
