ArticleeLife2020
Principles of operation of a cerebellar learning circuit.
Article in eLife, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed.
- Implicit sensorimotor adaptation comprises distinct mechanisms for action selection and execution.PLoS biology · 2026Article
- Brain-AI convergence: Generative world models and hierarchical attention for human intelligence.Patterns (New York, N.Y.) · 2026Review
- Modeling human visuomotor adaptation with a disturbance observer framework.PLoS computational biology · 2026Article
- [Relationship between cerebral dominance and learning styles in Anesthesiology residents].Revista medica del Instituto Mexicano del Seguro Social · 2026Observational
- Comparison of Signals from Cerebellar Purkinje Cells and Deep Nuclei during Temporal Prediction in Primates.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Contextual effects during sensorimotor adaptation are an emergent property of population coding in a cerebellar-inspired model.Science advances · 2025Article
- A normative principle governing memory transfer in cerebellar motor learning.Nature communications · 2025Article
- Cerebellar Micro Complex Model Using Histologic Boolean Mapping Simulates Adaptive Motor Control.Neuroinformatics · 2025Article
- Neural circuit mechanisms to transform cerebellar population dynamics for motor control in monkeys.bioRxiv : the preprint server for biology · 2025Article
- How neural systems transform synaptic plasticity into behavioral learning.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Synaptic weight dynamics underlying memory consolidation: Implications for learning rules, circuit organization, and circuit function.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Learning-to-learn as a metacognitive correlate of functional outcomes after stroke: a cohort study.European journal of physical and rehabilitation medicine · 2024Article
- A cerebellar population coding model for sensorimotor learning.bioRxiv : the preprint server for biology · 2024Article
- Cerebellar associative learning underlies skilled reach adaptation.Nature neuroscience · 2023Article
- An emergent temporal basis set robustly supports cerebellar time-series learning.Journal of neurophysiology · 2023Article
- Ventromedial Thalamus-Projecting DCN Neurons Modulate Associative Sensorimotor Responses in Mice.Neuroscience bulletin · 2022Article
- The influence of neck torsion and sequence of cycles on intra-trial reliability of smooth pursuit eye movement test in patients with neck pain disorders.Experimental brain research · 2022Article
- A cerebellum inspired spiking neural network as a multi-model for pattern classification and robotic trajectory prediction.Frontiers in neuroscience · 2022Article
- The Rules of Cerebellar Learning: Around the Ito Hypothesis.Neuroscience · 2021Review
- Diversity and dynamism in the cerebellum.Nature neuroscience · 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
4 authors.
Funding
Abstract
We provide behavioral evidence using monkey smooth pursuit eye movements for four principles of cerebellar learning. Using a circuit-level model of the cerebellum, we link behavioral data to learning's neural implementation. The four principles are: (1) early, fast, acquisition driven by climbing fiber inputs to the cerebellar cortex, with poor retention; (2) learned responses of Purkinje cells guide transfer of learning from the cerebellar cortex to the deep cerebellar nucleus, with excellent retention; (3) functionally different neural signals are subject to learning in the cerebellar cortex versus the deep cerebellar nuclei; and (4) negative feedback from the cerebellum to the inferior olive reduces the magnitude of the teaching signal in climbing fibers and limits learning. Our circuit-level model, based on these four principles, explains behavioral data obtained by strategically manipulating the signals responsible for acquisition and recall of direction learning in smooth pursuit eye movements across multiple timescales.
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.