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Incremental stability of spatiotemporal delayed dynamics and application to neural fields

机译:时空时滞动力学的增量稳定性及其在神经场中的应用

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We propose a Lyapounov-Krasovskii approach to study incremental stability in spatiotemporal dynamics with delays and their capability to be entrained by a periodic input. We then focus on delayed neural fields, which describe the spatiotemporal evolution of neuronal activity. We provide an explicit condition, involving the slope of the activation functions and the strength of coupling, under which delayed neural fields are incrementally stable regardless of communication delays. We finally show how this approach can be used to draw frequency profiles of neuronal populations.
机译:我们提出了一种Lyapounov-Krasovskii方法来研究时空动力学中具有时滞的增量稳定性及其被周期性输入所带动的能力。然后,我们将重点放在延迟的神经场上,这描述了神经元活动的时空演变。我们提供了一个明确的条件,涉及激活函数的斜率和耦合的强度,在这种条件下,无论通信延迟如何,延迟的神经场都是逐渐稳定的。我们最终展示了如何使用这种方法绘制神经元群体的频率分布图。

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