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Dopamine suppresses persistent network activity via D 1 ‐like dopamine receptors in rat medial entorhinal cortex
Author(s) -
Mayne Elizabeth W.,
Craig Michael T.,
McBain Chris J.,
Paulsen Ole
Publication year - 2013
Publication title -
european journal of neuroscience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.346
H-Index - 206
eISSN - 1460-9568
pISSN - 0953-816X
DOI - 10.1111/ejn.12125
Subject(s) - neuroscience , dopamine , ionotropic effect , entorhinal cortex , dopaminergic , inhibitory postsynaptic potential , glutamatergic , neurotransmission , excitatory postsynaptic potential , metabotropic receptor , dopamine receptor , chemistry , biology , receptor , medicine , glutamate receptor , hippocampus
Cortical networks display persistent activity in the form of periods of sustained synchronous depolarizations (‘ UP states’) punctuated by periods of relative hyperpolarization (‘ DOWN states’), which together form the slow oscillation. UP states are known to be synaptically generated and are sustained by a dynamic balance of excitation and inhibition, with fast ionotropic glutamatergic excitatory and GABA ergic inhibitory conductances increasing during the UP state. Previously, work from our group demonstrated that slow metabotropic GABA receptors also play an important role in terminating the UP state, but the effects of other neuromodulators on this network phenomenon have received little attention. Given that persistent activity is a neural correlate of working memory and that signalling through dopamine receptors has been shown to be critical for working memory tasks, we examined whether dopaminergic neurotransmission affected the slow oscillation. Here, using an in vitro model of the slow oscillation in rat medial entorhinal cortex, we showed that dopamine strongly and reversibly suppressed cortical UP states. We showed that this effect was mediated through D 1 ‐like and not D 2 ‐like dopamine receptors, and we found no evidence that tonic dopaminergic transmission affected UP states in our model.