偏微分方程形式的随机神经场的适定性与稳定性
Well-posedness and stability of a stochastic neural field in the form of a partial differential equation
AI总结:
针对随机神经场偏微分方程组因非线性、非局部性导致标准适定性理论失效的问题,本文将其转化为类Stefan自由边界问题,构造局部光滑解、证明全局存在性,并建立稳态解的非线性渐近稳定性。
摘要:
最近,人们提出了一个表示随机神经场的偏微分方程组,旨在模拟哺乳动物穿越物理空间时噪声网格细胞的活动。该系统由随机粒子系统严格推导而来,其噪声驱动的图案形成分岔已得到刻画。然而,由于其非线性和非局部性质,抛物方程光滑时间依赖解的标准适定性理论并不适用。在本文中,我们通过适当的变量变换将该问题转化为一个非线性类Stefan自由边界问题,并利用其表示公式在温和假设下构造局部时间光滑解。随后,我们证明快速衰减的初始条件和全局Lipschitz调制函数意味着解是全局时间的。最后,我们通过证明在适当假设下稳态解的非线性渐近稳定性,改进了以往的线性稳定性结果。
英文摘要:
A system of partial differential equations representing stochastic neural fields was recently proposed with the aim of modelling the activity of noisy grid cells when a mammal travels through physical space. The system was rigorously derived from a stochastic particle system and its noise-driven pattern-forming bifurcations have been characterised. However, due to its nonlinear and non-local nature, standard well-posedness theory for smooth time-dependent solutions of parabolic equations does not apply. In this article, we transform the problem through a suitable change of variables into a nonlinear Stefan-like free boundary problem and use its representation formulae to construct local-in-time smooth solutions under mild hypotheses. Then, we prove that fast-decaying initial conditions and globally Lipschitz modulation functions imply that solutions are global-in-time. Last, we improve previous linear stability results by showing nonlinear asymptotic stability of stationary solutions under suitable assumptions.
机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。