TAILIEUCHUNG - Journal of Mathematical Neuroscience (2011) 1:1 DOI 10.1186/2190-8567-1-1 RESEARCH Open

Journal of Mathematical Neuroscience (2011) 1:1 DOI RESEARCH Open Access Stability of the stationary solutions of neural field equations with propagation delays Romain Veltz · Olivier Faugeras Received: 22 October 2010 / Accepted: 3 May 2011 / Published online: 3 May 2011 © 2011 Veltz, Faugeras; licensee Springer. This is an Open Access article distributed under the terms of the Creative Commons Attribution License Abstract In this paper, we consider neural field equations with space-dependent delays. Neural fields are continuous assemblies of mesoscopic models arising when modeling macroscopic parts of the brain. They are modeled by nonlinear integrodifferential equations. We rigorously prove, for. | Journal of Mathematical Neuroscience 2011 1 1 DOI 2190-8567-1-1 0 The Journal of Mathematical Neuroscience a SpringerOpen Journal RESEARCH Open Access Stability of the stationary solutions of neural field equations with propagation delays Romain Veltz Olivier Faugeras Received 22 October 2010 Accepted 3 May 2011 Published online 3 May 2011 2011 Veltz Faugeras licensee Springer. This is an Open Access article distributed under the terms of the Creative Commons Attribution License Abstract In this paper we consider neural field equations with space-dependent delays. Neural fields are continuous assemblies of mesoscopic models arising when modeling macroscopic parts of the brain. They are modeled by nonlinear integrodifferential equations. We rigorously prove for the first time to our knowledge sufficient conditions for the stability of their stationary solutions. We use two methods 1 the computation of the eigenvalues of the linear operator defined by the linearized equations and 2 the formulation of the problem as a fixed point problem. The first method involves tools of functional analysis and yields a new estimate of the semigroup of the previous linear operator using the eigenvalues of its infinitesimal generator. It yields a sufficient condition for stability which is independent of the characteristics of the delays. The second method allows us to find new sufficient conditions for the stability of stationary solutions which depend upon the values of the delays. These conditions are very easy to evaluate numerically. We illustrate the conservativeness of the bounds with a comparison with numerical simulation. 1 Introduction Neural fields equations first appeared as a spatial-continuous extension of Hopfield networks with the seminal works of Wilson and Cowan Amari 1 2 . These networks describe the mean activity of neural populations by nonlinear integral equations and play an important role in the modeling of various cortical areas including the visual .

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