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Journal of Computational Neuroscience

Erschienen in:

01.10.2008

Fluctuation-driven rhythmogenesis in an excitatory neuronal network with slow adaptation

verfasst von: William H. Nesse, Alla Borisyuk, Paul C. Bressloff

Erschienen in: Journal of Computational Neuroscience | Ausgabe 2/2008

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Abstract

We study an excitatory all-to-all coupled network of N spiking neurons with synaptically filtered background noise and slow activity-dependent hyperpolarization currents. Such a system exhibits noise-induced burst oscillations over a range of values of the noise strength (variance) and level of cell excitability. Since both of these quantities depend on the rate of background synaptic inputs, we show how noise can provide a mechanism for increasing the robustness of rhythmic bursting and the range of burst frequencies. By exploiting a separation of time scales we also show how the system dynamics can be reduced to low-dimensional mean field equations in the limit N → ∞. Analysis of the bifurcation structure of the mean field equations provides insights into the dynamical mechanisms for initiating and terminating the bursts.

Vorheriger Artikel Slow oscillations in neural networks with facilitating synapses

Nächster Artikel Initiation and propagation of a neuronal intracellular calcium wave

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Titel: Fluctuation-driven rhythmogenesis in an excitatory neuronal network with slow adaptation
verfasst von: William H. Nesse
Alla Borisyuk
Paul C. Bressloff
Publikationsdatum: 01.10.2008
Verlag: Springer US
Erschienen in: Journal of Computational Neuroscience / Ausgabe 2/2008
Print ISSN: 0929-5313
Elektronische ISSN: 1573-6873
DOI: https://doi.org/10.1007/s10827-008-0081-y

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