Multi-day Neuron Tracking in High Density Electrophysiology Recordings using EMD
Update: 2023-08-05
Description
Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.08.03.551724v1?rss=1
Authors: Harris, T. D., Yuan, A. X., Colonell, J. I., Lebedeva, A., Charles, A.
Abstract:
Accurate tracking of the same neurons across multiple days is crucial for studying changes in neuronal activity during learning and adaptation. New 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, which 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 identify using measured visual receptive fields. This method succeeds on datasets separated from one to 47 days, with an 86% average recovery rate.
Copy rights belong to original authors. Visit the link for more info
Podcast created by Paper Player, LLC
http://biorxiv.org/cgi/content/short/2023.08.03.551724v1?rss=1
Authors: Harris, T. D., Yuan, A. X., Colonell, J. I., Lebedeva, A., Charles, A.
Abstract:
Accurate tracking of the same neurons across multiple days is crucial for studying changes in neuronal activity during learning and adaptation. New 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, which 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 identify using measured visual receptive fields. This method succeeds on datasets separated from one to 47 days, with an 86% average recovery rate.
Copy rights belong to original authors. Visit the link for more info
Podcast created by Paper Player, LLC
Comments
In Channel
Download from Google Play
Download from App Store
United States00:00
00:00
x
