Abstract
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.
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Notes
Let U, V be separable Hilbert spaces, \(L_{2}(V):=L_{2}(V,V)\), where \(L_{2}(U,V)\) denotes the space of Hilbert–Schmidt operators from U to V.
Or, more generally, with \(u_{0}\in L^{2}(\varOmega ,{\mathcal {F}}_{0},\mathbb {P};L^{2}({\mathcal {B}}))\).
That is, right-continuous with left limits.
For a topological space X, \({\mathcal {B}}(X)\) denotes the Borel\(\sigma \)-algebra.
See Da Prato and Zabczyk (2014, Chapter 9) for the definition of this notion.
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CK acknowledges financial support by a Lichtenberg Professorship.
JMT would like to thank Dirk Blömker for some useful comments.
Both authors are indebted to the anonymous reviewers for several helpful remarks.
Appendix A: Cylindrical Wiener processes in Hilbert spaces
Appendix A: Cylindrical Wiener processes in Hilbert spaces
Let \(\{v_{i}\}_{i\in \mathbb {N}}\subset H\) be a complete orthonormal system for H and let \(\{\beta _{t}^{i}\}_{i\in \mathbb {N}}\) be a collection of independent real-valued standard Brownian motions modeled on a filtered normal probability space \((\varOmega ,{\mathcal {F}},\{{\mathcal {F}}_{t}\}_{t\ge 0},\mathbb {P})\). Then the cylindrical Wiener process \(\{W_{t}\}_{t\ge 0}\) with covariance \(Q={\text {Id}}\) has the formal representation
which is a standard Wiener process in a weaker separable Hilbert space U, such that there exists a Hilbert–Schmidt embedding \(\iota :H\rightarrow U\), \(\iota \in L_{2}(H,U)\) and \(\{W_{t}\}_{t\ge 0}\) has the covariance operator \(\iota \iota ^{*}\). By Da Prato and Zabczyk (2014, Proposition 4.7 and Proposition 4.8), then \((\iota \iota ^{*})^{\frac{1}{2}}(U)=H\), and one can always find U and \(\iota \) with the above properties such that the representation (A.1) holds; see Da Prato and Zabczyk (2014, Chapter 4) for details. For our purposes, it is sufficient to set U equal to the abstract completion of \(L^{2}({\mathcal {B}})\) with respect to the alternative scalar product \((u,v)_{U}:=\sum _{n=1}^{\infty }n^{-2}\hat{u}_{n}\hat{v}_{n}\), where \(u,v\in H\) and \(\{\hat{u}_{n}\},\{\hat{v}_{n}\}\in \ell ^{2}\) are such that \(u=\sum _{n=1}^{\infty }\hat{u}_{n}v_{n}\) and \(v=\sum _{n=1}^{\infty }\hat{v}_{n}v_{n}\). Then \(\iota :={\text {Id}}\) is a Hilbert–Schmidt embedding from H into U.
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Kuehn, C., Tölle, J.M. A gradient flow formulation for the stochastic Amari neural field model. J. Math. Biol. 79, 1227–1252 (2019). https://doi.org/10.1007/s00285-019-01393-w
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DOI: https://doi.org/10.1007/s00285-019-01393-w
Keywords
- Gradient flow in nonlocal Hilbert space
- Stochastic Amari neural field equation
- Spatially correlated additive noise
- Space-time regularity of solutions
- Nonnegative kernel
- Unique invariant measure of the ergodic Feller semigroup