Evidence map›Paper›PMID 26432880›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2015

Control of cerebellar granule cell output by sensory-evoked Golgi cell inhibition.

Ian Duguid, Tiago Branco, Paul Chadderton, Charlotte Arlt, Kate Powell, Michael Häusser

Open access · bronzeAbstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.

0numbers the graph read from it
0cells of the map it votes in
47citing papers in PubMed
4.3field-weighted citation impact, top 6% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

47 citing papers in PubMed, 71 citations in OpenAlex.

  1. Review
  2. Review
  3. Article
  4. Effects of Short-Term Synaptic Plasticity in Feedforward Inhibitory Circuits on Cerebellar Responses to Repetitive Sensory Input.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026
    Article
  5. Review
  6. Article
  7. Article
  8. GlyT2-Positive Interneurons Regulate Timing and Variability of Information Transfer in a Cerebellar-Behavioral Loop.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Patch-seq: Advances and Biological Applications.Cellular and molecular neurobiology · 2023
    Review
  14. Article
  15. Article
  16. The Cerebellar Cortex.Annual review of neuroscience · 2022
    Review
  17. Article
  18. How and Why the Cerebellum Recodes Input Signals: An Alternative to Machine Learning.The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry · 2022
    Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Ian DuguidWolfson Institute for Biomedical Research and Department of Neuroscience, Physiology, and Pharmacology, University College London, London WC1E 6BT, United Kingdom ian.duguid@ed.ac.uk m.hausser@ucl.ac.uk.
Tiago BrancoWolfson Institute for Biomedical Research and Department of Neuroscience, Physiology, and Pharmacology, University College London, London WC1E 6BT, United Kingdom.
Paul ChaddertonWolfson Institute for Biomedical Research and Department of Neuroscience, Physiology, and Pharmacology, University College London, London WC1E 6BT, United Kingdom.
Charlotte ArltWolfson Institute for Biomedical Research and Department of Neuroscience, Physiology, and Pharmacology, University College London, London WC1E 6BT, United Kingdom.
Kate PowellWolfson Institute for Biomedical Research and Department of Neuroscience, Physiology, and Pharmacology, University College London, London WC1E 6BT, United Kingdom.
Michael HäusserWolfson Institute for Biomedical Research and Department of Neuroscience, Physiology, and Pharmacology, University College London, London WC1E 6BT, United Kingdom ian.duguid@ed.ac.uk m.hausser@ucl.ac.uk.
University College London · GB

Funding

Medical Research Council G1000512Medical Research Council MC_UP_1201/1Wellcome Trust 094077Wellcome Trust 098400
6 · The paper itself

Abstract

Classical feed-forward inhibition involves an excitation-inhibition sequence that enhances the temporal precision of neuronal responses by narrowing the window for synaptic integration. In the input layer of the cerebellum, feed-forward inhibition is thought to preserve the temporal fidelity of granule cell spikes during mossy fiber stimulation. Although this classical feed-forward inhibitory circuit has been demonstrated in vitro, the extent to which inhibition shapes granule cell sensory responses in vivo remains unresolved. Here we combined whole-cell patch-clamp recordings in vivo and dynamic clamp recordings in vitro to directly assess the impact of Golgi cell inhibition on sensory information transmission in the granule cell layer of the cerebellum. We show that the majority of granule cells in Crus II of the cerebrocerebellum receive sensory-evoked phasic and spillover inhibition prior to mossy fiber excitation. This preceding inhibition reduces granule cell excitability and sensory-evoked spike precision, but enhances sensory response reproducibility across the granule cell population. Our findings suggest that neighboring granule cells and Golgi cells can receive segregated and functionally distinct mossy fiber inputs, enabling Golgi cells to regulate the size and reproducibility of sensory responses.

Indexed as

AnimalsCerebellumCytoplasmic GranulesGolgi ApparatusPatch-Clamp TechniquesRatsRats, Sprague-DawleycerebellumGolgi cellsgranule cellsinhibitionsynaptic integration

Identifiers

PMID26432880
PMCPMC4620892
OpenAlexW2109164945

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.