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Stability of the stationary solutions of neural field equations with propagation delays

机译:具有传播时滞的神经场方程平稳解的稳定性。

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摘要

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 integro-differential 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.
机译:在本文中,我们考虑具有空间相关延迟的神经场方程。神经场是对大脑的宏观部分建模时产生的介观模型的连续集合。它们通过非线性积分微分方程建模。据我们所知,我们首次严格证明了其固定解稳定性的充分条件。我们使用两种方法:1)计算由线性化方程式定义的线性算子的特征值,以及2)将问题表示为不动点问题。第一种方法涉及功能分析工具,并使用其无穷小生成器的特征值产生对先前线性算子的半群的新估计。它产生了足够的稳定性条件,而与延迟的特性无关。第二种方法使我们能够找到依赖于时延值的固定解稳定性的新的充分条件。这些条件很容易用数字评估。我们通过与数值模拟的比较说明边界的保守性。

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