ArticleCell2025
A deep learning strategy to identify cell types across species from high-density extracellular recordings.
Article in Cell, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed.
- Article
- Predictive and instructive cerebellar encoding of dopamine reward drives motivated behavior.Nature neuroscience · 2026Article
- Large-scale in vivo electrophysiology analysis reveals opposing thalamic and hippocampal excitability after third-trimester-equivalent alcohol exposure in mice.Journal of neurophysiology · 2026Article
- Application of Machine Learning Models to Identify Differences in Neural Electrophysiological Properties Across Estrous Cycle Phases.Neuroinformatics · 2026Article
- Optotagging in primate cortex uncovers excitatory and inhibitory neuron specializations in form vision.Nature communications · 2026Article
- HIPPIE: a generative model for electrophysiological analysis across species, technologies, and modalities.Nature communications · 2026Article
- Large-scale electrophysiology at single-spike resolution.Nature reviews. Neuroscience · 2026Review
- Neural circuits encode prior knowledge of temporal statistics.Nature neuroscience · 2026Article
- Article
- A multimodal approach for visualizing and identifying electrophysiological cell types in vivo.Nature communications · 2026Article
- Extracellular Spike Waveforms: Morphology, Biophysics, and Classification Strategies.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Review
- Robotic cell transportation system based on micropipette resistance modeling.Microsystems & nanoengineering · 2026Article
- Consensus Paper: Models of Cerebellar Functions.Cerebellum (London, England) · 2026Review
- Complementary cortical and thalamic contributions to cell type-specific striatal activity dynamics during movement.Science advances · 2026Article
- Beyond Transplantation: Engineering Neural Cell Therapies and Combination Strategies for Spinal Cord Repair.Brain sciences · 2026Review
- Ultra-high-density Neuropixels probes improve detection and identification in neuronal recordings.Neuron · 2025Article
- Article
- SLAy-ing oversplitting errors in high-density electrophysiology spike sorting.bioRxiv : the preprint server for biology · 2025Article
- Mapping neural subspace dynamics onto the structure of the mouse descending motor system.bioRxiv : the preprint server for biology · 2025Article
- Article
Corrections and comments
- Update of
Authors and funding
23 authors.
Funding
Abstract
High-density probes allow electrophysiological recordings from many neurons simultaneously across entire brain circuits but fail to reveal cell type. Here, we develop a strategy to identify cell types from extracellular recordings in awake animals and reveal the computational roles of neurons with distinct functional, molecular, and anatomical properties. We combine optogenetics 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 the 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 species. Our classifier extends the power of modern dynamical systems analyses by revealing the unique contributions of simultaneously recorded cell types during behavior.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.