Burst-Dependent Bidirectional Plasticity in the Cerebellum Is Driven by Presynaptic NMDA Receptors.
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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.
Mice, Inbred C57BL
Excitatory Postsynaptic Potentials
Published Version (Please cite this version)10.1016/j.celrep.2016.03.004
Publication InfoBouvier, 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.
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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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