Evidence map›Paper›PMID 36599827›Full record

ArticleNature communications2023

Cerebro-cerebellar networks facilitate learning through feedback decoupling.

Ellen Boven, Joseph Pemberton, Paul Chadderton, Richard Apps, Rui Ponte Costa

Open access · goldAbstract read
PubMed Publisher
In one paragraph

Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
5.0field-weighted citation impact, top 4% 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

30 citing papers in PubMed, 42 citations in OpenAlex.

  1. Trial
  2. Trial
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  4. Review
  5. Article
  6. The Cerebellar Connectome.Cerebellum (London, England) · 2026
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Synergistic Reinforcement Learning by Cooperation of the Cerebellum and Basal Ganglia.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
    Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Review
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

5 authors at 1 institution in 1 country.

Ellen Boven *Bristol Computational Neuroscience Unit, Intelligent Systems Labs, SCEEM, Faculty of Engineering, University of Bristol, Bristol, BS8 1TH, UK.
Joseph Pemberton *Bristol Computational Neuroscience Unit, Intelligent Systems Labs, SCEEM, Faculty of Engineering, University of Bristol, Bristol, BS8 1TH, UK.
Paul ChaddertonSchool of Physiology, Pharmacology and Neuroscience, Faculty of Life Sciences, University of Bristol, Bristol, BS8 1TH, UK.ORCID 0000-0002-6276-1011
Richard AppsSchool of Physiology, Pharmacology and Neuroscience, Faculty of Life Sciences, University of Bristol, Bristol, BS8 1TH, UK.ORCID 0000-0002-2064-3359
Rui Ponte CostaBristol Computational Neuroscience Unit, Intelligent Systems Labs, SCEEM, Faculty of Engineering, University of Bristol, Bristol, BS8 1TH, UK. rui.costa@bristol.ac.uk.ORCID 0000-0003-2595-2027
University of Bristol · GB

Funding

Medical Research Council G1100626Medical Research Council MR/X006107/1Wellcome Trust
6 · The paper itself

Abstract

Behavioural feedback is critical for learning in the cerebral cortex. However, such feedback is often not readily available. How the cerebral cortex learns efficiently despite the sparse nature of feedback remains unclear. Inspired by recent deep learning algorithms, we introduce a systems-level computational model of cerebro-cerebellar interactions. In this model a cerebral recurrent network receives feedback predictions from a cerebellar network, thereby decoupling learning in cerebral networks from future feedback. When trained in a simple sensorimotor task the model shows faster learning and reduced dysmetria-like behaviours, in line with the widely observed functional impact of the cerebellum. Next, we demonstrate that these results generalise to more complex motor and cognitive tasks. Finally, the model makes several experimentally testable predictions regarding cerebro-cerebellar task-specific representations over learning, task-specific benefits of cerebellar predictions and the differential impact of cerebellar and inferior olive lesions. Overall, our work offers a theoretical framework of cerebro-cerebellar networks as feedback decoupling machines.

Indexed as

CerebellumCerebral CortexCerebellar NucleiFeedbackNerve Net

Identifiers

PMID36599827
OpenAlexW4313494122

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.