ArticleFrontiers in computational neuroscience2016
Distributed Cerebellar Motor Learning: A Spike-Timing-Dependent Plasticity Model.
Article in Frontiers in computational neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 46 citations in OpenAlex.
- A computational model of the cerebellar granular layer calibrated to experimental data for studying inhibition and sensory encoding.Scientific reports · 2025Article
- Cerebellar contribution to multisensory integration: A computational modeling exploration.APL bioengineering · 2025Article
- Coincidence detection between apical and basal dendrites drives STDP in cerebellar Golgi cells.Communications biology · 2025Article
- Mesoscale simulations predict the role of synergistic cerebellar plasticity during classical eyeblink conditioning.PLoS computational biology · 2024Article
- Brain-Inspired Spiking Neural Network Controller for a Neurorobotic Whisker System.Frontiers in neurorobotics · 2022Article
- A cerebellum inspired spiking neural network as a multi-model for pattern classification and robotic trajectory prediction.Frontiers in neuroscience · 2022Article
- Cerebellum Involvement in Dystonia During Associative Motor Learning: Insights From a Data-Driven Spiking Network Model.Frontiers in systems neuroscience · 2022Article
- Towards a Bio-Inspired Real-Time Neuromorphic Cerebellum.Frontiers in cellular neuroscience · 2021Article
- Inhibitory Plasticity: From Molecules to Computation and Beyond.International journal of molecular sciences · 2020Review
- Spike burst-pause dynamics of Purkinje cells regulate sensorimotor adaptation.PLoS computational biology · 2019Article
- The Concept of Transmission Coefficient Among Different Cerebellar Layers: A Computational Tool for Analyzing Motor Learning.Frontiers in neural circuits · 2019Article
- A Biomimetic Control Method Increases the Adaptability of a Humanoid Robot Acting in a Dynamic Environment.Frontiers in neurorobotics · 2019Article
- Hebbian Spike-Timing Dependent Plasticity at the Cerebellar Input Stage.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2017Article
- Event- and Time-Driven Techniques Using Parallel CPU-GPU Co-processing for Spiking Neural Networks.Frontiers in neuroinformatics · 2017Article
- Modeling the Cerebellar Microcircuit: New Strategies for a Long-Standing Issue.Frontiers in cellular neuroscience · 2016Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep cerebellar nuclei neurons receive both inhibitory (GABAergic) synaptic currents from Purkinje cells (within the cerebellar cortex) and excitatory (glutamatergic) synaptic currents from mossy fibers. Those two deep cerebellar nucleus inputs are thought to be also adaptive, embedding interesting properties in the framework of accurate movements. We show that distributed spike-timing-dependent plasticity mechanisms (STDP) located at different cerebellar sites (parallel fibers to Purkinje cells, mossy fibers to deep cerebellar nucleus cells, and Purkinje cells to deep cerebellar nucleus cells) in close-loop simulations provide an explanation for the complex learning properties of the cerebellum in motor learning. Concretely, we propose a new mechanistic cerebellar spiking model. In this new model, deep cerebellar nuclei embed a dual functionality: deep cerebellar nuclei acting as a gain adaptation mechanism and as a facilitator for the slow memory consolidation at mossy fibers to deep cerebellar nucleus synapses. Equipping the cerebellum with excitatory (e-STDP) and inhibitory (i-STDP) mechanisms at deep cerebellar nuclei afferents allows the accommodation of synaptic memories that were formed at parallel fibers to Purkinje cells synapses and then transferred to mossy fibers to deep cerebellar nucleus synapses. These adaptive mechanisms also contribute to modulate the deep-cerebellar-nucleus-output firing rate (output gain modulation toward optimizing its working range).
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