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arXiv 1807.02575math.APmath.DSmath.FAmath.PRq-bio.NC
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随机Amari神经场模型的梯度流公式

A gradient flow formulation for the stochastic Amari neural field model

Christian Kuehn, Jonas M. Tölle

摘要:

我们研究随机Amari型神经场方程,这是平均场模型的神经活动在大脑皮层。我们证明了在一定的耦合核假设下,神经场模型可以看作是非局部希尔伯特空间中的梯度流。这使得所有梯度流方法都可用于分析,而以前无法使用这些方法,因为不知道是否存在严格的梯度流公式。我们发现,方程是适定的非局部希尔伯特空间在这个意义上说,在这个空间中开始的解决方案也保持在它的所有时间和时空规律性的结果举行的情况下,空间相关的噪声。讨论了不变测度的唯一性、相关Feller半群的遍历性以及核的几个例子。

英文摘要:

We study stochastic Amari-type neural field equations, which are mean-field models for neural activity in the cortex. We prove that under certain assumptions on the coupling kernel, the neural field model can be viewed as a gradient flow in a nonlocal Hilbert space. This makes all gradient flow methods available for the analysis, which could previously not be used, as it was not known, whether a rigorous gradient flow formulation exists. We show that the equation is well-posed in the nonlocal Hilbert space in the sense that solutions starting in this space also remain in it for all times and space-time regularity results hold for the case of spatially correlated noise. Uniqueness of invariant measures, ergodic properties for the associated Feller semigroups, and several examples of kernels are also discussed.

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。

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