Marie E. Hemelt
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Marie E. Hemelt has a total of 22 co-authors. Here are the top co-authors by number of shared publications:
- Agoston Lajko
Wolfson Institute for Biomedical Research, University College London
1 shared publication - Alvaro Sánchez-López
Department of Neuroscience, Baylor College of Medicine
1 shared publication - Beverley A. Clark
University College London
1 shared publication - Court Hull
Department of Neurobiology, Duke University Medical School, Durham, NC, USA.
1 shared publication - Dana Cohen
The Leslie and Susan Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel
1 shared publication - David J. Herzfeld
Department of Neurobiology, Duke University School of Medicine
1 shared publication
Latest Publications
- Open Access
A deep-learning strategy to identify cell types across species from high-density extracellular recordings
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.
Open AccessA deep-learning strategy to identify cell types across species from high-density extracellular recordings
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.