ArticlePLoS computational biology2011
Stimulus-dependent state transition between synchronized oscillation and randomly repetitive burst in a model cerebellar granular layer.
Article in PLoS computational biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 28 citations in OpenAlex.
- Emergence of brain-inspired small-world spiking neural network through neuroevolution.iScience · 2024Article
- Regulating synchronous oscillations of cerebellar granule cells by different types of inhibition.PLoS computational biology · 2021Article
- Modulation of the dynamics of cerebellar Purkinje cells through the interaction of excitatory and inhibitory feedforward pathways.PLoS computational biology · 2021Article
- Cerebellar Cortex 4-12 Hz Oscillations and Unit Phase Relation in the Awake Rat.Frontiers in systems neuroscience · 2020Article
- Spike burst-pause dynamics of Purkinje cells regulate sensorimotor adaptation.PLoS computational biology · 2019Article
- Spatiotemporal network coding of physiological mossy fiber inputs by the cerebellar granular layer.PLoS computational biology · 2017Article
- Gap Junction Modulation of Low-Frequency Oscillations in the Cerebellar Granule Cell Layer.Cerebellum (London, England) · 2017Article
- Distributed Cerebellar Motor Learning: A Spike-Timing-Dependent Plasticity Model.Frontiers in computational neuroscience · 2016Article
- Control of cerebellar granule cell output by sensory-evoked Golgi cell inhibition.Proceedings of the National Academy of Sciences of the United States of America · 2015Article
- At the Edge of Chaos: How Cerebellar Granular Layer Network Dynamics Can Provide the Basis for Temporal Filters.PLoS computational biology · 2015Article
- Spike-coding mechanisms of cerebellar temporal processing in classical conditioning and voluntary movements.Cerebellum (London, England) · 2014Review
- Fast convergence of learning requires plasticity between inferior olive and deep cerebellar nuclei in a manipulation task: a closed-loop robotic simulation.Frontiers in computational neuroscience · 2014Review
- Article
- An internal model architecture for novelty detection: implications for cerebellar and collicular roles in sensory processing.PloS one · 2012Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Information processing of the cerebellar granular layer composed of granule and Golgi cells is regarded as an important first step toward the cerebellar computation. Our previous theoretical studies have shown that granule cells can exhibit random alternation between burst and silent modes, which provides a basis of population representation of the passage-of-time (POT) from the onset of external input stimuli. On the other hand, another computational study has reported that granule cells can exhibit synchronized oscillation of activity, as consistent with observed oscillation in local field potential recorded from the granular layer while animals keep still. Here we have a question of whether an identical network model can explain these distinct dynamics. In the present study, we carried out computer simulations based on a spiking network model of the granular layer varying two parameters: the strength of a current injected to granule cells and the concentration of Mg²⁺ which controls the conductance of NMDA channels assumed on the Golgi cell dendrites. The simulations showed that cells in the granular layer can switch activity states between synchronized oscillation and random burst-silent alternation depending on the two parameters. For higher Mg²⁺ concentration and a weaker injected current, granule and Golgi cells elicited spikes synchronously (synchronized oscillation state). In contrast, for lower Mg²⁺ concentration and a stronger injected current, those cells showed the random burst-silent alternation (POT-representing state). It is suggested that NMDA channels on the Golgi cell dendrites play an important role for determining how the granular layer works in response to external input.
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