ArticleeLife2022
Deep learning-based feature extraction for prediction and interpretation of sharp-wave ripples in the rodent hippocampus.
Article in eLife, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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
27 citing papers in PubMed.
- Magnetic resonance identification tags for ultra-flexible electrodes.Nature communications · 2026Article
- Global coincident bursts of high frequency oscillations across the human cortex coordinate large-scale memory processing.Nature communications · 2026Article
- Spatiotemporal patterns differentiate hippocampal sharp-wave ripples from interictal epileptiform discharges in mice and humans.Nature communications · 2025Article
- Ripple contributions to human memory: making the spiking content count.Nature reviews. Neuroscience · 2025Review
- Review
- Evaluation of new quality productive forces in Henan province based on improved entropy weight-TOPSIS method and deep learning.Scientific reports · 2025Article
- Behavioral Timing of Interictal Spikes, But Not Rate, Correlates with Impaired Working Memory Performance.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Article
- Noncanonical circuits, states, and computations of the hippocampus.Science (New York, N.Y.) · 2025Review
- Ultraslow serotonin oscillations in the hippocampus delineate substates across NREM and waking.eLife · 2025Article
- Article
- The role of electroencephalography in epilepsy research-From seizures to interictal activity and comorbidities.Epilepsia · 2025Review
- Self-supervised learning reduces label noise in sharp wave ripple classification.Scientific reports · 2025Article
- Open-Source Algorithm for Automated Vigilance State Classification Using Single-Channel Electroencephalogram in Rodents.Sensors (Basel, Switzerland) · 2025Article
- Deep learning-based automated detection of endometrioid endometrial carcinoma in histopathology.Frontiers in oncology · 2025Article
- Article
- Interictal spikes during spatial working memory carry helpful or distracting representations of space and have opposing impacts on performance.bioRxiv : the preprint server for biology · 2024Article
- Months-long tracking of neuronal ensembles spanning multiple brain areas with Ultra-Flexible Tentacle Electrodes.Nature communications · 2024Article
- PyHFO: lightweight deep learning-powered end-to-end high-frequency oscillations analysis application.Journal of neural engineering · 2024Article
- Cross-strata co-occurrence of ripples with theta-frequency oscillations in the hippocampus of foraging rats.The Journal of physiology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Local field potential (LFP) deflections and oscillations define hippocampal sharp-wave ripples (SWRs), one of the most synchronous events of the brain. SWRs reflect firing and synaptic current sequences emerging from cognitively relevant neuronal ensembles. While spectral analysis have permitted advances, the surge of ultra-dense recordings now call for new automatic detection strategies. Here, we show how one-dimensional convolutional networks operating over high-density LFP hippocampal recordings allowed for automatic identification of SWR from the rodent hippocampus. When applied without retraining to new datasets and ultra-dense hippocampus-wide recordings, we discovered physiologically relevant processes associated to the emergence of SWR, prompting for novel classification criteria. To gain interpretability, we developed a method to interrogate the operation of the artificial network. We found it relied in feature-based specialization, which permit identification of spatially segregated oscillations and deflections, as well as synchronous population firing typical of replay. Thus, using deep learning-based approaches may change the current heuristic for a better mechanistic interpretation of these relevant neurophysiological events.
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.