ReviewAnnual review of neuroscience2018
Computational Principles of Supervised Learning in the Cerebellum.
Review in Annual review of neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 129 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
129 citing papers in PubMed.
- Predictive and instructive cerebellar encoding of dopamine reward drives motivated behavior.Nature neuroscience · 2026Article
- Brain-AI convergence: Generative world models and hierarchical attention for human intelligence.Patterns (New York, N.Y.) · 2026Review
- Cerebellum-inspired neural network of supervised learning with tensor-based sparse coding for multi-class classification.PLoS computational biology · 2026Article
- Unsupervised nonmotor learning in the human cerebellum.bioRxiv : the preprint server for biology · 2026Article
- The Human Cerebellum Encodes Temporally Sensitive Reinforcement Learning Signals.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Critical period plasticity enables credit assignment.bioRxiv : the preprint server for biology · 2026Article
- Neural circuits encode prior knowledge of temporal statistics.Nature neuroscience · 2026Article
- Synchronous climbing fiber activity enables instructive signaling for cerebellar learning through modulation of disinhibitory circuits.Nature neuroscience · 2026Article
- Protracted prediction: Neurodevelopment of reward processing in the adolescent cerebellum.bioRxiv : the preprint server for biology · 2026Article
- Cerebellar perturbation impairs human working memory and degrades spatial tuning throughout cortex.bioRxiv : the preprint server for biology · 2026Article
- Article
- Behavioral timescale synaptic plasticity: properties, elements and functions.Nature neuroscience · 2026Review
- A Digital Anatomical Atlas of the Human Cerebellum at Subfolial Resolution.Human brain mapping · 2026Article
- Consensus Paper: Models of Cerebellar Functions.Cerebellum (London, England) · 2026Review
- Beyond synaptic plasticity: a summary of a linear model of the cerebellar locomotor computation.Frontiers in neural circuits · 2026Review
- A transient feature of the inferior olive supports the development of cerebellar internal models.bioRxiv : the preprint server for biology · 2025Article
- Contextual effects during sensorimotor adaptation are an emergent property of population coding in a cerebellar-inspired model.Science advances · 2025Article
- A theory of cerebellar learning as spike-based reinforcement learning in continuous time and space.PNAS nexus · 2025Article
- Reward-driven cerebellar climbing fiber activity influences both neural and behavioral learning.Current biology : CB · 2025Article
- The human cerebellum encodes temporally sensitive reinforcement learning signals.bioRxiv : the preprint server for biology · 2025Article
69 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Supervised learning plays a key role in the operation of many biological and artificial neural networks. Analysis of the computations underlying supervised learning is facilitated by the relatively simple and uniform architecture of the cerebellum, a brain area that supports numerous motor, sensory, and cognitive functions. We highlight recent discoveries indicating that the cerebellum implements supervised learning using the following organizational principles: ( a) extensive preprocessing of input representations (i.e., feature engineering), ( b) massively recurrent circuit architecture, ( c) linear input-output computations, ( d) sophisticated instructive signals that can be regulated and are predictive, ( e) adaptive mechanisms of plasticity with multiple timescales, and ( f) task-specific hardware specializations. The principles emerging from studies of the cerebellum have striking parallels with those in other brain areas and in artificial neural networks, as well as some notable differences, which can inform future research on supervised learning and inspire next-generation machine-based algorithms.
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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.