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Mechanisms and functional roles of glutamatergic synapse diversity in a cerebellar circuit.

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Date
2016-09-19
Authors
Zampini, Valeria
Liu, Jian K
Diana, Marco A
Maldonado, Paloma P
Brunel, Nicolas
Dieudonné, Stéphane
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Abstract
Synaptic currents display a large degree of heterogeneity of their temporal characteristics, but the functional role of such heterogeneities remains unknown. We investigated in rat cerebellar slices synaptic currents in Unipolar Brush Cells (UBCs), which generate intrinsic mossy fibers relaying vestibular inputs to the cerebellar cortex. We show that UBCs respond to sinusoidal modulations of their sensory input with heterogeneous amplitudes and phase shifts. Experiments and modeling indicate that this variability results both from the kinetics of synaptic glutamate transients and from the diversity of postsynaptic receptors. While phase inversion is produced by an mGluR2-activated outward conductance in OFF-UBCs, the phase delay of ON UBCs is caused by a late rebound current resulting from AMPAR recovery from desensitization. Granular layer network modeling indicates that phase dispersion of UBC responses generates diverse phase coding in the granule cell population, allowing climbing-fiber-driven Purkinje cell learning at arbitrary phases of the vestibular input.
Type
Journal article
Subject
AMPA receptor
cerebellum
computational model
desensitization
metabotropic receptor
neuroscience
rat
sensory processing
Permalink
https://hdl.handle.net/10161/15104
Published Version (Please cite this version)
10.7554/eLife.15872
Publication Info
Zampini, Valeria; Liu, Jian K; Diana, Marco A; Maldonado, Paloma P; Brunel, Nicolas; & Dieudonné, Stéphane (2016). Mechanisms and functional roles of glutamatergic synapse diversity in a cerebellar circuit. Elife, 5. 10.7554/eLife.15872. Retrieved from https://hdl.handle.net/10161/15104.
This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.
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Scholars@Duke

Brunel

Nicolas Brunel

Professor of Neurobiology
We use theoretical models of brain systems to investigate how they process and learn information from their inputs. Our current work focuses on the mechanisms of learning and memory, from the synapse to the network level, in collaboration with various experimental groups. Using methods fromstatistical physics, we have shown recently that the synapticconnectivity of a network that maximizes storage capacity reproducestwo key experimentally observed features: low connection proba
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