Abstract
Neurons of different types perform diverse computations and coordinate their activity during sensation, perception, and action. While electrophysiological recordings can measure the activity of many neurons simultaneously, identifying cell types during these experiments remains difficult. Here we present PhysMAP, a framework adapted from multiomics data analysis that weights multiple electrophysiological modalities simultaneously to obtain interpretable multimodal representations. We apply PhysMAP to seven datasets and demonstrate that these multimodal representations are better aligned with known transcriptomically-defined cell types than any single modality alone. We then show that this alignment allows PhysMAP to better identify putative cell types in the absence of ground truth. We also demonstrate how annotated datasets can transfer labels to unannotated recordings and confirm that inferred cell types exhibit properties consistent with ground truth. Crucially, we show that PhysMAP can also be used to iteratively detect batch effects which confound classification. Together, these results establish PhysMAP as a tool for studying multiple cell types simultaneously and gaining insight into neural circuit dynamics.
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Data availability
Post-processed data used in analyses and figure generation are provided in the repository https://github.com/EricKenjiLee/PhysMAP_Manuscript. Raw datasets are available from each source publication’s open data repositories in Table 1. Additionally, source data for all non-schematic main and Supplementary Figs. are provided as pages in a “Source Data” Excel file. Source data are provided with this paper.
Code availability
MATLAB, Python, and R for open dataset analysis and figure generation are available at https://github.com/EricKenjiLee/PhysMAP_Manuscript and is citable at https://doi.org/10.5281/zenodo.18239113.
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Acknowledgements
We are grateful to Dr. Karel Svoboda, Dr. Anna Lakunina, and Dr. Josh Siegle for the initial ideas of cell type classifiers for electrophysiology. We also thank Dr. Adam Smoulder, Dr. Yujin Han, Dr. Josh Siegle, Dr. Ching Fang, Pierre Boucher, Nicole Carr, Dr. Tian Wang, Vivian Moosmann, Tushar Arora, Mateo Umaguing, and Dr. Munib Hasnain for their thoughtful comments on our manuscript. CC was supported by an NIH NINDS R00NS092972, R01NS121409, R21NS135361 and R01NS122969 award; the Moorman-Simon Interdisciplinary Career Development Professorship from Boston University; the Whitehall Foundation (2019-12-77); and the Young Investigator Award from the Brain and Behavior Research Foundation (27923). The auditory cortex dataset (collected by AL and SJ) was supported by an NIH NIDCD R01DC015531. SJ was also supported by an NIH NINDS RF1NS131993. EKL was supported by an NIH NINDS F31NS131018. The Neuropixels Ultras dataset was supported by NIH NINDS/NIMH U01NS113252 awarded to NS.
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E.K.L. and C.C. jointly developed the multi-modal cell type classifier approach. A.E.G. helped with derived waveform metric analysis and several control analyses. G.H. helped collect one of the open datasets and provided technical advice, especially with regard to the usage of quality metrics, and helped edit initial manuscript drafts. H.Y. and C.H. provided many useful discussions, suggested analyses, and provided access to several datasets. A.L. and S.J. provided technical support and also collected the A1 open datasets. A.S., S.O., and N.A.S. provided technical support and collected the Ultras dataset. P.F.P. provided guidance on various approaches for multi-modal integration and other technical advice. E.K.L. curated datasets. C.C. wrote initial R code and performed some analyses. Code was further refined and augmented by E.K.L. Additional analyses were also written by E.K.L. E.K.L. made figures and wrote initial drafts of the paper with C.C. All authors edited the manuscript and provided feedback on presentation and clarity.
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Lee, E.K., Gül, A.E., Heller, G. et al. A multimodal approach for visualizing and identifying electrophysiological cell types in vivo. Nat Commun (2026). https://doi.org/10.1038/s41467-026-71331-0
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DOI: https://doi.org/10.1038/s41467-026-71331-0
