RT Journal Article SR Electronic T1 PhysMAP - interpretable in vivo neuronal cell type identification using multi-modal analysis of electrophysiological data JF bioRxiv FD Cold Spring Harbor Laboratory SP 2024.02.28.582461 DO 10.1101/2024.02.28.582461 A1 Lee, Eric Kenji A1 Gül, Asım Emre A1 Heller, Greggory A1 Lakunina, Anna A1 Jaramillo, Santiago A1 Przytycki, Pawel F. A1 Chandrasekaran, Chandramouli YR 2024 UL http://biorxiv.org/content/early/2024/02/28/2024.02.28.582461.abstract AB Cells of different types perform diverse computations and coordinate their activity during sensation, perception, and action. While electrophysiological approaches can measure the activity of many neurons simultaneously, assigning cell type labels to these neurons is an open problem. Here, we develop PhysMAP, a framework that weighs multiple electrophysiological modalities simultaneously in an unsupervised manner and obtain an interpretable representation that separates neurons by cell type. PhysMAP is superior to any single electrophysiological modality in identifying neuronal cell types such as excitatory pyramidal, PV+ interneurons, and SOM+ interneurons with high confidence in both juxtacellular and extracellular recordings and from multiple areas of the mouse brain. PhysMAP built on ground truth data can be used for classifying cell types in new and existing electrophysiological datasets, and thus facilitate simultaneous assessment of the coordinated dynamics of multiple neuronal cell types during behavior.Competing Interest StatementThe authors have declared no competing interest.