ArticleeLife2021
Interpreting wide-band neural activity using convolutional neural networks.
Article in eLife, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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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.
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Who cites it
13 citing papers in PubMed, 29 citations in OpenAlex.
- Deciphering hippocampal place codes in weak theta rhythms.Nature communications · 2026Article
- DTCNet: finger flexion decoding with three-dimensional ECoG data.Frontiers in computational neuroscience · 2025Article
- A machine learning toolbox for the analysis of sharp-wave ripples reveals common waveform features across species.Communications biology · 2024Article
- End-to-end deep learning approach to mouse behavior classification from cortex-wide calcium imaging.PLoS computational biology · 2024Article
- A machine learning toolbox for the analysis of sharp-wave ripples reveal common features across species.bioRxiv : the preprint server for biology · 2023Article
- Article
- How our understanding of memory replay evolves.Journal of neurophysiology · 2023Review
- A rubric for human-like agents and NeuroAI.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Review
- Predictive maps in rats and humans for spatial navigation.Current biology : CB · 2022Article
- Article
- Magnetic resonance-based eye tracking using deep neural networks.Nature neuroscience · 2021Article
- Uncovering spatial representations from spatiotemporal patterns of rodent hippocampal field potentials.Cell reports methods · 2021Article
- Improving scalability in systems neuroscience.Neuron · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 4 institutions in 3 countries.
Funding
Abstract
Rapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings. Even so, interpretation of this data requires considerable knowledge about the nature of the representation and often depends on manual operations. Decoding provides a means to infer the information content of such recordings but typically requires highly processed data and prior knowledge of the encoding scheme. Here, we developed a deep-learning framework able to decode sensory and behavioral variables directly from wide-band neural data. The network requires little user input and generalizes across stimuli, behaviors, brain regions, and recording techniques. Once trained, it can be analyzed to determine elements of the neural code that are informative about a given variable. We validated this approach using electrophysiological and calcium-imaging data from rodent auditory cortex and hippocampus as well as human electrocorticography (ECoG) data. We show successful decoding of finger movement, auditory stimuli, and spatial behaviors - including a novel representation of head direction - from raw neural activity.
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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.