A realistic large-scale model of the cerebellum granular layer predicts circuit spatio-temporal filtering properties.
about
Modeling the Cerebellar Microcircuit: New Strategies for a Long-Standing IssueThe cerebellar Golgi cell and spatiotemporal organization of granular layer activityA Hybrid Model for the Computationally-Efficient Simulation of the Cerebellar Granular Layer.Distributed Cerebellar Motor Learning: A Spike-Timing-Dependent Plasticity Model.The generation of phase differences and frequency changes in a network model of inferior olive subthreshold oscillationsModeling spike-train processing in the cerebellum granular layer and changes in plasticity reveal single neuron effects in neural ensemblesDistributed cerebellar plasticity implements adaptable gain control in a manipulation task: a closed-loop robotic simulationA bi-hemispheric neuronal network model of the cerebellum with spontaneous climbing fiber firing produces asymmetrical motor learning during robot controlExploring the significance of morphological diversity for cerebellar granule cell excitability.Local field potential modeling predicts dense activation in cerebellar granule cells clusters under LTP and LTD controlFast convergence of learning requires plasticity between inferior olive and deep cerebellar nuclei in a manipulation task: a closed-loop robotic simulation.Regulation of output spike patterns by phasic inhibition in cerebellar granule cellsThe spatiotemporal organization of cerebellar network activity resolved by two-photon imaging of multiple single neurons.Synaptic glutamate spillover increases NMDA receptor reliability at the cerebellar glomerulus.Adaptive robotic control driven by a versatile spiking cerebellar networkAt the Edge of Chaos: How Cerebellar Granular Layer Network Dynamics Can Provide the Basis for Temporal Filters.Increased number of cerebellar granule cells and astrocytes in the internal granule layer in sheep following prenatal intra-amniotic injection of lipopolysaccharide.NMDA receptors with incomplete Mg²⁺ block enable low-frequency transmission through the cerebellar cortex.Defective cerebellar control of cortical plasticity in writer's crampComputational Modeling of Single Neuron Extracellular Electric Potentials and Network Local Field Potentials using LFPsimRealistic modeling of neurons and networks: towards brain simulation.Evaluation of Teaching Signals for Motor Control in the Cerebellum during Real-World Robot Application.Integration and regulation of glomerular inhibition in the cerebellar granular layer circuit.Single Neuron Optimization as a Basis for Accurate Biophysical Modeling: The Case of Cerebellar Granule Cells.Discovery and rediscoveries of Golgi cells.Cerebellar motor learning versus cerebellar motor timing: the climbing fibre story.θ-Frequency resonance at the cerebellum input stage improves spike timing on the millisecond time-scale.Spike timing regulation on the millisecond scale by distributed synaptic plasticity at the cerebellum input stage: a simulation study.A realistic bi-hemispheric model of the cerebellum uncovers the purpose of the abundant granule cells during motor control.High frequency burst firing of granule cells ensures transmission at the parallel fiber to purkinje cell synapse at the cost of temporal coding.Robustness effect of gap junctions between Golgi cells on cerebellar cortex oscillations.High-Pass Filtering and Dynamic Gain Regulation Enhance Vertical Bursts Transmission along the Mossy Fiber Pathway of Cerebellum.Rebuilding cerebellar network computations from cellular neurophysiologyAction potential processing in a detailed Purkinje cell model reveals a critical role for axonal compartmentalization.Model cerebellar granule cells can faithfully transmit modulated firing rate signals.Spatiotemporal network coding of physiological mossy fiber inputs by the cerebellar granular layer.The number of granular cells in a cerebellar neuronal network model engaged during robot control increases with the complexity of the motor task.Ensemble neuronal responses in a large-scale realistic model of the cerebellar cortex.Event time representation in cerebellar mossy fibres arising from the lateral reticular nucleus.Morphological Constraints on Cerebellar Granule Cell Combinatorial Diversity.
P2860
Q26741612-DA3A91A1-CB7C-482D-96A8-F67F66F29030Q26995006-C500F071-D929-473A-91E6-D631B2EA1D76Q27318597-AB1DE00F-4D1C-4148-924D-D66C9D9B8885Q27318621-3254B5C7-EFC2-4B7C-9C96-12EC5E3CB84DQ27330260-46B263A8-E846-471B-998A-E5D3C5D40258Q30525475-609BFC17-58D8-4228-89BE-67273286422CQ30549902-E7D8AFF3-E4A6-44DF-AEB5-528854114596Q30596928-36D03083-CB99-45A9-99FF-755D5DE3DF25Q33559962-1C0E7BF9-0B1A-4FC7-BBDA-7A148253FB4AQ33983007-A36D3E53-B46B-4493-9425-44C6D042930FQ34049001-6F0726F5-C946-42B4-9BDE-71FED96A5A48Q34081160-23590974-4FA6-4872-9F8C-DFAD883ECAADQ35159987-0BD477FE-F012-4458-90E0-17CEA53580BEQ35317159-A5DDEC0F-596E-4990-A802-AFED2B054B66Q35413391-7DF0CAA1-84AB-4867-9D39-C10B1B9561B0Q35814396-65D13D05-966B-4BA9-A97A-4A595CB3DCFBQ35851340-DC21FA0B-3FEB-46B2-95D2-588F408E56F0Q36296133-4C8C36CF-7348-4514-AFB0-6761C2F56B16Q36953848-CAF0F5F7-8FFB-4068-BD95-6048DEEBAE7DQ37043281-AE8B262E-3B14-43CC-A264-20CBD8961A14Q37267191-8A23E8D7-03D5-4937-A01B-08CBA0159492Q37533716-924FBB34-1252-42BF-A604-BA162703B5C9Q37601683-531814D3-BD15-4ED5-885F-706E6090023FQ37699599-0BD10718-633C-4C97-AC45-D97EC59F5777Q37767960-25BDF926-1A60-48AC-8A0D-172977A385C1Q37864435-0762CE2C-1D00-49E8-93AC-1C46E6DE2D31Q39735433-A93CA286-785A-4237-9C97-4F2B035D4BE3Q40114273-379A7F26-0AD3-4883-8575-7227347BEE13Q40151804-CDAEB77B-B9DE-42D0-A191-A55E08FB0744Q40868728-D8816C09-37ED-410E-AA52-BEC0326D671DQ41070163-6B5F03F5-EDFD-4FA8-B96F-6608AA76D2B8Q41274259-3D4D9C4C-B5CC-483F-B806-4A1AA613A68EQ41729023-990ED4AD-0CDB-42B8-8C52-5257CA0E2926Q41857645-D0059744-2ADA-4D84-8028-FAD947925F31Q42283095-DD515962-42C1-4C29-84F2-59A117B91BE4Q42369646-12FC8BAF-F88D-4CF8-BA6D-49A73F94C5ACQ45824669-D2A2CD25-D295-4702-8D36-C017211A9857Q46137971-42732B69-B3C2-4661-B635-26ACAFE9F253Q46355363-FB46B0DD-388B-4D07-9220-8D97711165BBQ47586045-3071D8EC-31FC-4420-ACAE-F4BBB66AEB5B
P2860
A realistic large-scale model of the cerebellum granular layer predicts circuit spatio-temporal filtering properties.
description
2010 nî lūn-bûn
@nan
2010年の論文
@ja
2010年学术文章
@wuu
2010年学术文章
@zh-cn
2010年学术文章
@zh-hans
2010年学术文章
@zh-my
2010年学术文章
@zh-sg
2010年學術文章
@yue
2010年學術文章
@zh
2010年學術文章
@zh-hant
name
A realistic large-scale model ...... temporal filtering properties.
@en
type
label
A realistic large-scale model ...... temporal filtering properties.
@en
prefLabel
A realistic large-scale model ...... temporal filtering properties.
@en
P2860
P356
P1476
A realistic large-scale model ...... temporal filtering properties.
@en
P2093
Sergio Solinas
P2860
P356
10.3389/FNCEL.2010.00012
P577
2010-05-14T00:00:00Z