Evidence map›Paper›PMID 38870929›Full record

ArticleNeuron2024

A cerebellar granule cell-climbing fiber computation to learn to track long time intervals.

Martha G Garcia-Garcia, Akash Kapoor, Oluwatobi Akinwale, Lina Takemaru, Tony Hyun Kim, Casey Paton, Ashok Litwin-Kumar, Mark J Schnitzer, Liqun Luo, Mark J Wagner

Abstract read
In one paragraph

Article in Neuron, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
–field-weighted citation impact
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

20 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Multi-timescale Computation by Astrocytes.bioRxiv : the preprint server for biology · 2026
    Article
  10. Consensus Paper: Models of Cerebellar Functions.Cerebellum (London, England) · 2026
    Review
  11. Brain-Behavior Relationships: Neural Mechanisms of Impulsivity and Compulsivity in Neuropsychiatric Disorders.Cognitive and behavioral neurology : official journal of the Society for Behavioral and Cognitive Neurology · 2025
    Review
  12. Article
  13. Review
  14. Article
  15. Review
  16. Article
  17. Article
  18. Social and emotional learning in the cerebellum.Nature reviews. Neuroscience · 2024
    Review
  19. A preprocessing toolbox for 2-photon subcellular calcium imaging.bioRxiv : the preprint server for biology · 2024
    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

10 authors.

Martha G Garcia-GarciaNational Institute of Neurological Disorders & Stroke, National Institutes of Health, Bethesda, MD 20894, USA.
Akash KapoorNational Institute of Neurological Disorders & Stroke, National Institutes of Health, Bethesda, MD 20894, USA.
Oluwatobi AkinwaleNational Institute of Neurological Disorders & Stroke, National Institutes of Health, Bethesda, MD 20894, USA.
Lina TakemaruNational Institute of Neurological Disorders & Stroke, National Institutes of Health, Bethesda, MD 20894, USA.
Tony Hyun KimDepartment of Biology and Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA.
Casey PatonNational Institute of Neurological Disorders & Stroke, National Institutes of Health, Bethesda, MD 20894, USA.
Ashok Litwin-KumarZuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA.
Mark J SchnitzerDepartment of Biology and Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA; Department of Applied Physics, Stanford University, Stanford, CA 94305, USA.
Liqun LuoDepartment of Biology and Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA.
Mark J WagnerNational Institute of Neurological Disorders & Stroke, National Institutes of Health, Bethesda, MD 20894, USA. Electronic address: mark.wagner@nih.gov.

Funding

Neocortex-cerebellum Circuitry Unit ZIANS009434 · NINDS · NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE · PI WAGNER, MARK · 2022 to 2025
$8.8M
Mosaic Analysis with Double Markers in MiceR01NS050835 · NINDS · STANFORD UNIVERSITY · PI LIQUN LUO · 2005 to 2026
$7.3M
Howard Hughes Medical InstituteIntramural NIH HHS ZIA NS009434NINDS NIH HHS R01 NS050835
6 · The paper itself

Abstract

In classical cerebellar learning, Purkinje cells (PkCs) associate climbing fiber (CF) error signals with predictive granule cells (GrCs) that were active just prior (∼150 ms). The cerebellum also contributes to behaviors characterized by longer timescales. To investigate how GrC-CF-PkC circuits might learn seconds-long predictions, we imaged simultaneous GrC-CF activity over days of forelimb operant conditioning for delayed water reward. As mice learned reward timing, numerous GrCs developed anticipatory activity ramping at different rates until reward delivery, followed by widespread time-locked CF spiking. Relearning longer delays further lengthened GrC activations. We computed CF-dependent GrC→PkC plasticity rules, demonstrating that reward-evoked CF spikes sufficed to grade many GrC synapses by anticipatory timing. We predicted and confirmed that PkCs could thereby continuously ramp across seconds-long intervals from movement to reward. Learning thus leads to new GrC temporal bases linking predictors to remote CF reward signals-a strategy well suited for learning to track the long intervals common in cognitive domains.

Indexed as

CerebellumLearningPurkinje CellsRewardAction PotentialsAnimalsConditioning, OperantMaleMiceMice, Inbred C57BLNerve FibersNeuronal PlasticityNeuronsTime Factorsassociative learningcerebellumclimbing fibersgranule cellsneural rampingoperant conditioningreward learningtemporal encodingtemporal learningtwo-photon imaging

Identifiers

PMID38870929
PMCPMC11343686

What OpenQuestion holds

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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.