Burst-Dependent Bidirectional Plasticity in the Cerebellum Is Driven by Presynaptic NMDA Receptors.
Abstract
Numerous studies have shown that cerebellar function is related to the plasticity
at the synapses between parallel fibers and Purkinje cells. How specific input patterns
determine plasticity outcomes, as well as the biophysics underlying plasticity of
these synapses, remain unclear. Here, we characterize the patterns of activity that
lead to postsynaptically expressed LTP using both in vivo and in vitro experiments.
Similar to the requirements of LTD, we find that high-frequency bursts are necessary
to trigger LTP and that this burst-dependent plasticity depends on presynaptic NMDA
receptors and nitric oxide (NO) signaling. We provide direct evidence for calcium
entry through presynaptic NMDA receptors in a subpopulation of parallel fiber varicosities.
Finally, we develop and experimentally verify a mechanistic plasticity model based
on NO and calcium signaling. The model reproduces plasticity outcomes from data and
predicts the effect of arbitrary patterns of synaptic inputs on Purkinje cells, thereby
providing a unified description of plasticity.
Type
Journal articleSubject
Purkinje CellsPresynaptic Terminals
Animals
Mice, Inbred C57BL
Mice
Rats
Rats, Wistar
Nitric Oxide
Receptors, N-Methyl-D-Aspartate
Calcium Signaling
Excitatory Postsynaptic Potentials
Action Potentials
Long-Term Potentiation
Models, Neurological
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https://hdl.handle.net/10161/23353Published Version (Please cite this version)
10.1016/j.celrep.2016.03.004Publication Info
Bouvier, Guy; Higgins, David; Spolidoro, Maria; Carrel, Damien; Mathieu, Benjamin;
Léna, Clément; ... Casado, Mariano (2016). Burst-Dependent Bidirectional Plasticity in the Cerebellum Is Driven by Presynaptic
NMDA Receptors. Cell reports, 15(1). pp. 104-116. 10.1016/j.celrep.2016.03.004. Retrieved from https://hdl.handle.net/10161/23353.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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Show full item recordScholars@Duke
Nicolas Brunel
Duke School of Medicine Distinguished Professor in Neuroscience
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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