ReviewFrontiers in computational neuroscience2014
Fast convergence of learning requires plasticity between inferior olive and deep cerebellar nuclei in a manipulation task: a closed-loop robotic simulation.
Review in Frontiers in computational neuroscience, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed, 48 citations in OpenAlex.
- Changes in Cerebellar Multiunit Activity Associated with Ventrolateral Striatal Injury During Spontaneous Motor Behavior.Medical sciences (Basel, Switzerland) · 2026Article
- Cerebellar Contributions to Hypokinetic Symptoms in an Acute Lesion Parkinsonism Model.Neurology international · 2025Article
- NeuroVis: Real-Time Neural Information Measurement and Visualization of Embodied Neural Systems.Frontiers in neural circuits · 2021Article
- Normal-Appearing Cerebellar Damage in Neuromyelitis Optica Spectrum Disorder.AJNR. American journal of neuroradiology · 2019Article
- Spike burst-pause dynamics of Purkinje cells regulate sensorimotor adaptation.PLoS computational biology · 2019Article
- Long-Lasting Response Changes in Deep Cerebellar NucleiFrontiers in cellular neuroscience · 2019Article
- A Biomimetic Control Method Increases the Adaptability of a Humanoid Robot Acting in a Dynamic Environment.Frontiers in neurorobotics · 2019Article
- The interplay between cerebellum and basal ganglia in motor adaptation: A modeling study.PloS one · 2019Article
- Synaptic excitation by climbing fibre collaterals in the cerebellar nuclei of juvenile and adult mice.The Journal of physiology · 2017Article
- Event- and Time-Driven Techniques Using Parallel CPU-GPU Co-processing for Spiking Neural Networks.Frontiers in neuroinformatics · 2017Article
- Cerebellar-inspired algorithm for adaptive control of nonlinear dielectric elastomer-based artificial muscle.Journal of the Royal Society, Interface · 2016Article
- Distributed Circuit Plasticity: New Clues for the Cerebellar Mechanisms of Learning.Cerebellum (London, England) · 2016Review
- Whole-Cell Properties of Cerebellar Nuclei Neurons In Vivo.PloS one · 2016Article
- Modeling the Cerebellar Microcircuit: New Strategies for a Long-Standing Issue.Frontiers in cellular neuroscience · 2016Review
- Distributed Cerebellar Motor Learning: A Spike-Timing-Dependent Plasticity Model.Frontiers in computational neuroscience · 2016Article
- Sick of sitting.Diabetologia · 2015Review
- Integrated plasticity at inhibitory and excitatory synapses in the cerebellar circuit.Frontiers in cellular neuroscience · 2015Review
- Distributed cerebellar plasticity implements generalized multiple-scale memory components in real-robot sensorimotor tasks.Frontiers in computational neuroscience · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The cerebellum is known to play a critical role in learning relevant patterns of activity for adaptive motor control, but the underlying network mechanisms are only partly understood. The classical long-term synaptic plasticity between parallel fibers (PFs) and Purkinje cells (PCs), which is driven by the inferior olive (IO), can only account for limited aspects of learning. Recently, the role of additional forms of plasticity in the granular layer, molecular layer and deep cerebellar nuclei (DCN) has been considered. In particular, learning at DCN synapses allows for generalization, but convergence to a stable state requires hundreds of repetitions. In this paper we have explored the putative role of the IO-DCN connection by endowing it with adaptable weights and exploring its implications in a closed-loop robotic manipulation task. Our results show that IO-DCN plasticity accelerates convergence of learning by up to two orders of magnitude without conflicting with the generalization properties conferred by DCN plasticity. Thus, this model suggests that multiple distributed learning mechanisms provide a key for explaining the complex properties of procedural learning and open up new experimental questions for synaptic plasticity in the cerebellar network.
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.