ArticlePLoS computational biology2015
At the Edge of Chaos: How Cerebellar Granular Layer Network Dynamics Can Provide the Basis for Temporal Filters.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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25 citing papers in PubMed, 52 citations in OpenAlex.
- Modeling the rhythmic complexity of professional drumming with an oscillation-driven reservoir computer.Cognitive neurodynamics · 2026Article
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- To be and not to be: wide-field Ca2+ imaging reveals neocortical functional segmentation combines stability and flexibility.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- The contribution of the basal ganglia and cerebellum to motor learning: A neuro-computational approach.PLoS computational biology · 2023Article
- A cerebellum inspired spiking neural network as a multi-model for pattern classification and robotic trajectory prediction.Frontiers in neuroscience · 2022Article
- Computational models of neurotransmission at cerebellar synapses unveil the impact on network computation.Frontiers in computational neuroscience · 2022Review
- Multidimensional population activity in an electrically coupled inhibitory circuit in the cerebellar cortex.Neuron · 2021Article
- Parameter tuning differentiates granule cell subtypes enriching transmission properties at the cerebellum input stage.Communications biology · 2020Article
- Article
- Toward New Modalities in VEP-Based BCI Applications Using Dynamical Stimuli: Introducing Quasi-Periodic and Chaotic VEP-Based BCI.Frontiers in neuroscience · 2020Article
- A generative learning model for saccade adaptation.PLoS computational biology · 2019Article
- Review
- Motor context dominates output from purkinje cell functional regions during reflexive visuomotor behaviours.eLife · 2019Article
- Optimal modularity and memory capacity of neural reservoirs.Network neuroscience (Cambridge, Mass.) · 2019Article
- Article
- Computational Principles of Supervised Learning in the Cerebellum.Annual review of neuroscience · 2018Review
- Conditional deletion of Cadherin 13 perturbs Golgi cells and disrupts social and cognitive behaviors.Genes, brain, and behavior · 2018Article
- Neonatal Hyperoxia Perturbs Neuronal Development in the Cerebellum.Molecular neurobiology · 2018Article
- Modeling the Encoding of Saccade Kinematic Metrics in the Purkinje Cell Layer of the Cerebellar Vermis.Frontiers in computational neuroscience · 2018Article
- Article
Corrections and comments
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3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Models of the cerebellar microcircuit often assume that input signals from the mossy-fibers are expanded and recoded to provide a foundation from which the Purkinje cells can synthesize output filters to implement specific input-signal transformations. Details of this process are however unclear. While previous work has shown that recurrent granule cell inhibition could in principle generate a wide variety of random outputs suitable for coding signal onsets, the more general application for temporally varying signals has yet to be demonstrated. Here we show for the first time that using a mechanism very similar to reservoir computing enables random neuronal networks in the granule cell layer to provide the necessary signal separation and extension from which Purkinje cells could construct basis filters of various time-constants. The main requirement for this is that the network operates in a state of criticality close to the edge of random chaotic behavior. We further show that the lack of recurrent excitation in the granular layer as commonly required in traditional reservoir networks can be circumvented by considering other inherent granular layer features such as inverted input signals or mGluR2 inhibition of Golgi cells. Other properties that facilitate filter construction are direct mossy fiber excitation of Golgi cells, variability of synaptic weights or input signals and output-feedback via the nucleocortical pathway. Our findings are well supported by previous experimental and theoretical work and will help to bridge the gap between system-level models and detailed models of the granular layer network.
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