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A Microscopic Spiking Neuronal Network for the Age-Structured Model

A Microscopic Spiking Neuronal Network for the Age-Structured Model We introduce a microscopic spiking network consistent with the age-structured/renewal equation proposed by Pakdaman, Perthame and Salort. It is a jump process interacting through a set of global activity variables with random delays. We show the well-posedness of the particle system and the mean-field equation. Moreover, by studying the tightness of the empirical measure, we prove the propagation of chaos property. Eventually, we quantify the rate of convergence by using the coupling method. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Acta Applicandae Mathematicae Springer Journals

A Microscopic Spiking Neuronal Network for the Age-Structured Model

Acta Applicandae Mathematicae , Volume 146 (1) – May 4, 2016

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References (28)

Publisher
Springer Journals
Copyright
Copyright © 2016 by Springer Science+Business Media Dordrecht
Subject
Mathematics; Mathematics, general; Computer Science, general; Theoretical, Mathematical and Computational Physics; Complex Systems; Classical Mechanics
ISSN
0167-8019
eISSN
1572-9036
DOI
10.1007/s10440-016-0056-3
Publisher site
See Article on Publisher Site

Abstract

We introduce a microscopic spiking network consistent with the age-structured/renewal equation proposed by Pakdaman, Perthame and Salort. It is a jump process interacting through a set of global activity variables with random delays. We show the well-posedness of the particle system and the mean-field equation. Moreover, by studying the tightness of the empirical measure, we prove the propagation of chaos property. Eventually, we quantify the rate of convergence by using the coupling method.

Journal

Acta Applicandae MathematicaeSpringer Journals

Published: May 4, 2016

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