ArticleNature neuroscience2021
A database and deep learning toolbox for noise-optimized, generalized spike inference from calcium imaging.
Article in Nature neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers.
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81 citing papers in PubMed.
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- Noninvasive methods to monitor dynamic single-cell events.Trends in cell biology · 2026Review
- A large-scale dataset of functional mouse ganglion cell layer responses.Scientific data · 2026Article
- Spike inference from calcium imaging data acquired with GCaMP8 indicators.Nature methods · 2026Article
- Psychedelic drug action at dendrites is gated by behavioral state and serotonin receptors.bioRxiv : the preprint server for biology · 2026Article
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- Precise calcium-to-spike inference using biophysical generative models.bioRxiv : the preprint server for biology · 2026Article
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- Microfluidic platforms for probing spontaneous functional recovery in hierarchically modular neuronal networks.Communications engineering · 2026Article
- Dynamic neuronal ensembles encode burst-suppression revealed by cortex-wide optical-electrical interfaces.Nature communications · 2026Article
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- LUMIN: an automated graphical analysis toolbox for high-throughput calcium imaging of in vitro neuronal cultures.Scientific reports · 2026Article
- Voltage imaging as a window into neural computation.Neurophotonics · 2026Article
- Principles of cortical interactions in modular recurrent networks.bioRxiv : the preprint server for biology · 2026Article
- Neural substrate of conditioned stimulus for associative learning in the hippocampus.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Bridging Model and Experiment in Systems Neuroscience with Cleo: The Closed-Loop, Electrophysiology, and Optophysiology Simulation Testbed.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
21 more citing papers are in PubMed but not listed here.
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11 authors.
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Abstract
Inference of action potentials ('spikes') from neuronal calcium signals is complicated by the scarcity of simultaneous measurements of action potentials and calcium signals ('ground truth'). In this study, we compiled a large, diverse ground truth database from publicly available and newly performed recordings in zebrafish and mice covering a broad range of calcium indicators, cell types and signal-to-noise ratios, comprising a total of more than 35 recording hours from 298 neurons. We developed an algorithm for spike inference (termed CASCADE) that is based on supervised deep networks, takes advantage of the ground truth database, infers absolute spike rates and outperforms existing model-based algorithms. To optimize performance for unseen imaging data, CASCADE retrains itself by resampling ground truth data to match the respective sampling rate and noise level; therefore, no parameters need to be adjusted by the user. In addition, we developed systematic performance assessments for unseen data, openly released a resource toolbox and provide a user-friendly cloud-based implementation.
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