Dataset Information

A deep-learning strategy to identify cell types across species from high-density extracellular recordings.

ABSTRACT: High-density probes allow electrophysiological recordings from many neurons simultaneously across entire brain circuits but don't reveal cell type. Here, we develop a strategy to identify cell types from extracellular recordings in awake animals, revealing the computational roles of neurons with distinct functional, molecular, and anatomical properties. We combine optogenetic activation and pharmacology using the cerebellum as a testbed to generate a curated ground-truth library of electrophysiological properties for Purkinje cells, molecular layer interneurons, Golgi cells, and mossy fibers. We train a semi-supervised deep-learning classifier that predicts cell types with greater than 95% accuracy based on waveform, discharge statistics, and layer of the recorded neuron. The classifier's predictions agree with expert classification on recordings using different probes, in different laboratories, from functionally distinct cerebellar regions, and across animal species. Our classifier extends the power of modern dynamical systems analyses by revealing the unique contributions of simultaneously-recorded cell types during behavior.

SUBMITTER: Beau M

PROVIDER: S-EPMC10862837 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature

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A deep-learning strategy to identify cell types across species from high-density extracellular recordings.

Beau Maxime M Herzfeld David J DJ Naveros Francisco F Hemelt Marie E ME D'Agostino Federico F Oostland Marlies M Sánchez-López Alvaro A Chung Young Yoon YY Michael Maibach Kyranakis Stephen S Stabb Hannah N HN Martínez Lopera M Gabriela MG Lajko Agoston A Zedler Marie M Ohmae Shogo S Hall Nathan J NJ Clark Beverley A BA Cohen Dana D Lisberger Stephen G SG Kostadinov Dimitar D Hull Court C Häusser Michael M Medina Javier F JF

bioRxiv : the preprint server for biology 20240505

High-density probes allow electrophysiological recordings from many neurons simultaneously across entire brain circuits but don't reveal cell type. Here, we develop a strategy to identify cell types from extracellular recordings in awake animals, revealing the computational roles of neurons with distinct functional, molecular, and anatomical properties. We combine optogenetic activation and pharmacology using the cerebellum as a testbed to generate a curated ground-truth library of electrophysio ...[more]

PMID: 38352514

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Dataset Information

A deep-learning strategy to identify cell types across species from high-density extracellular recordings.

Publications

A deep-learning strategy to identify cell types across species from high-density extracellular recordings.

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