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.

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Citation

Published Version (Please cite this version)

10.1016/j.celrep.2016.03.004

Publication Info

Bouvier, Guy, David Higgins, Maria Spolidoro, Damien Carrel, Benjamin Mathieu, Clément Léna, Stéphane Dieudonné, Boris Barbour, et al. (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.

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Scholars@Duke

Brunel

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 from
statistical physics, we have shown recently that the synaptic
connectivity of a network that maximizes storage capacity reproduces
two key experimentally observed features: low connection probability
and strong overrepresentation of bidirectionnally connected pairs of
neurons. We have also inferred `synaptic plasticity rules' (a
mathematical description of how synaptic strength depends on the
activity of pre and post-synaptic neurons) from data, and shown that
networks endowed with a plasticity rule inferred from data have a
storage capacity that is close to the optimal bound.



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