ArticleNature communications2018
A cerebellar mechanism for learning prior distributions of time intervals.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
- Article
- The Human Cerebellum Encodes Temporally Sensitive Reinforcement Learning Signals.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Neural circuits encode prior knowledge of temporal statistics.Nature neuroscience · 2026Article
- Cerebellar contributions to action and cognition: Prediction, timescale, and continuity.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Concomitant motor responses facilitate the acquisition of multiple timing priors beyond upper-limb contexts.iScience · 2026Article
- Granule cells reorient cortical manifolds to separate contexts but preserve their geometry.bioRxiv : the preprint server for biology · 2026Article
- Communication between the medial frontal cortex and mediodorsal thalamus is required for timing performance in rats.Neurobiology of learning and memory · 2026Article
- Contextual effects during sensorimotor adaptation are an emergent property of population coding in a cerebellar-inspired model.Science advances · 2025Article
- The human cerebellum encodes temporally sensitive reinforcement learning signals.bioRxiv : the preprint server for biology · 2025Article
- Cerebellar circuit computations for predictive motor control.Nature reviews. Neuroscience · 2025Review
- Contextual neural dynamics during time perception in the primate ventral premotor cortex.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Neuronal dynamics of cerebellum and medial prefrontal cortex in adaptive motor timing.Nature communications · 2025Article
- Perturbation context in paced finger tapping tunes the error-correction mechanism.Scientific reports · 2024Article
- Rapid calibration to dynamic temporal contexts.Quarterly journal of experimental psychology (2006) · 2024Article
- Body-part specificity for learning of multiple prior distributions in human coincidence timing.NPJ science of learning · 2024Article
- A cerebellar population coding model for sensorimotor learning.bioRxiv : the preprint server for biology · 2024Article
- Neuroimaging Signatures of Metacognitive Improvement in Sensorimotor Timing.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Emergence of syntax and word prediction in an artificial neural circuit of the cerebellum.Nature communications · 2024Article
- Keeping time and rhythm by internal simulation of sensory stimuli and behavioral actions.Science advances · 2024Article
- Purkinje cell models: past, present and future.Frontiers in computational neuroscience · 2024Review
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Authors and funding
4 authors.
Funding
Abstract
Knowledge about the statistical regularities of the world is essential for cognitive and sensorimotor function. In the domain of timing, prior statistics are crucial for optimal prediction, adaptation and planning. Where and how the nervous system encodes temporal statistics is, however, not known. Based on physiological and anatomical evidence for cerebellar learning, we develop a computational model that demonstrates how the cerebellum could learn prior distributions of time intervals and support Bayesian temporal estimation. The model shows that salient features observed in human Bayesian time interval estimates can be readily captured by learning in the cerebellar cortex and circuit level computations in the cerebellar deep nuclei. We test human behavior in two cerebellar timing tasks and find prior-dependent biases in timing that are consistent with the predictions of the cerebellar model.
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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.