Synaptic plasticity rules with physiological calcium levels.
Abstract
Spike-timing-dependent plasticity (STDP) is considered as a primary mechanism underlying
formation of new memories during learning. Despite the growing interest in activity-dependent
plasticity, it is still unclear whether synaptic plasticity rules inferred from in
vitro experiments are correct in physiological conditions. The abnormally high calcium
concentration used in in vitro studies of STDP suggests that in vivo plasticity rules
may differ significantly from in vitro experiments, especially since STDP depends
strongly on calcium for induction. We therefore studied here the influence of extracellular
calcium on synaptic plasticity. Using a combination of experimental (patch-clamp recording
and Ca2+ imaging at CA3-CA1 synapses) and theoretical approaches, we show here that the classic
STDP rule in which pairs of single pre- and postsynaptic action potentials induce
synaptic modifications is not valid in the physiological Ca2+ range. Rather, we found that these pairs of single stimuli are unable to induce any
synaptic modification in 1.3 and 1.5 mM calcium and lead to depression in 1.8 mM.
Plasticity can only be recovered when bursts of postsynaptic spikes are used, or when
neurons fire at sufficiently high frequency. In conclusion, the STDP rule is profoundly
altered in physiological Ca2+, but specific activity regimes restore a classical STDP profile.
Type
Journal articleSubject
AnimalsRats, Wistar
Calcium
Action Potentials
Neuronal Plasticity
Long-Term Potentiation
Nonlinear Dynamics
Models, Neurological
Time Factors
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https://hdl.handle.net/10161/23342Published Version (Please cite this version)
10.1073/pnas.2013663117Publication Info
Inglebert, Yanis; Aljadeff, Johnatan; Brunel, Nicolas; & Debanne, Dominique (2020). Synaptic plasticity rules with physiological calcium levels. Proceedings of the National Academy of Sciences of the United States of America, 117(52). pp. 33639-33648. 10.1073/pnas.2013663117. Retrieved from https://hdl.handle.net/10161/23342.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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