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Erschienen in: Journal of Computational Neuroscience 1/2009

01.02.2009

Dendrites determine the contribution of after depolarization potentials (ADPs) to generation of repetitive action potentials in hypothalamic gonadotropin releasing-hormone (GnRH) neurons

verfasst von: C. B. Roberts, M. P. O’Boyle, K. J. Suter

Erschienen in: Journal of Computational Neuroscience | Ausgabe 1/2009

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Abstract

The impact of structure in modulating synaptic signals originating in dendrites is widely recognized. In this study, we focused on the impact of dendrite morphology on a local spike generating mechanism which has been implicated in hormone secretion, the after depolarization potential (ADP). Using multi-compartmental models of hypothalamic GnRH neurons, we systematically truncated dendrite length and determined the consequence on ADP amplitude and repetitive firing. Decreasing the length of the dendrite significantly increased the amplitude of the ADP and increased repetitive firing. These effects were observed in dendrites both with and without active conductances suggesting they largely reflect passive characteristics of the dendrite. In order to test the findings of the model, we performed whole-cell recordings in GnRH neurons and elicited ADPs using current injection. During recordings, neurons were filled with biocytin so that we could determine dendritic and total projection (dendrite plus axon) length. Neurons exhibited ADPs and increasing ADP amplitude was associated with decreasing dendrite length, in keeping with the predictions of the models. Thus, despite the relatively simple morphology of the GnRH neuron’s dendrite, it can still exert a substantial impact on the final neuronal output.

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Metadaten
Titel
Dendrites determine the contribution of after depolarization potentials (ADPs) to generation of repetitive action potentials in hypothalamic gonadotropin releasing-hormone (GnRH) neurons
verfasst von
C. B. Roberts
M. P. O’Boyle
K. J. Suter
Publikationsdatum
01.02.2009
Verlag
Springer US
Erschienen in
Journal of Computational Neuroscience / Ausgabe 1/2009
Print ISSN: 0929-5313
Elektronische ISSN: 1573-6873
DOI
https://doi.org/10.1007/s10827-008-0095-5

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