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A gradient flow formulation for the stochastic Amari neural field model

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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

  1. Let UV 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.

  2. Or, more generally, with \(u_{0}\in L^{2}(\varOmega ,{\mathcal {F}}_{0},\mathbb {P};L^{2}({\mathcal {B}}))\).

  3. That is, right-continuous with left limits.

  4. For a topological space X, \({\mathcal {B}}(X)\) denotes the Borel\(\sigma \)-algebra.

  5. See Da Prato and Zabczyk (2014, Chapter 9) for the definition of this notion.

References

  • Achleitner F, Kuehn C (2015) On bounded positive stationary solutions for a nonlocal Fisher-KPP equation. Nonlinear Anal Theory Methods Appl 112:15–29

    MathSciNet  MATH  Google Scholar 

  • Amari S (1977) Dynamics of pattern formation in lateral-inhibition type neural fields. Biol Cybern 27:77–87

    MathSciNet  MATH  Google Scholar 

  • Ambrosio L, Gigli N, Savaré G (2006) Gradient flows: in metric spaces and in the space of probability measures. Birkhäuser, Basel

    MATH  Google Scholar 

  • Bachmair C, Schöll E (2014) Nonlocal control of pulse propagation in excitable media. Eur Phys J B 87(11):276

    MathSciNet  Google Scholar 

  • Barbu V, Da Prato G (2006) Ergodicity for nonlinear stochastic equations in variational formulation. Appl Math Optim 53(2):121–139

    MathSciNet  MATH  Google Scholar 

  • Barret F (2015) Sharp asymptotics of metastable transition times for one-dimensional SPDEs. Ann Inst Henri Poincaré Probab Stat 51(1):129–166

    MathSciNet  MATH  Google Scholar 

  • Berestycki H, Nadin G, Perthame B, Ryzhik L (2009) The non-local Fisher-KPP equation: travelling waves and steady states. Nonlinearity 22:2813–2844

    MathSciNet  MATH  Google Scholar 

  • Berglund N (2013) Kramers’ law: validity, derivations and generalisations. Markov Process Relat Fields 19(3):459–490

    MathSciNet  MATH  Google Scholar 

  • Berglund N, Gentz B (2013) Sharp estimates for metastable lifetimes in parabolic SPDEs: Kramers’ law and beyond. Electron J Probab 18(24):1–58

    MathSciNet  MATH  Google Scholar 

  • Berglund N, Gesù GD, Weber H (2017) An Eyring–Kramers law for the stochastic Allen–Cahn equation in dimension two. Electron J Probab 22:1–27

    MathSciNet  MATH  Google Scholar 

  • Bogachev VI (1998) Gaussian measures. American Mathematical Society, Rhode Island

    MATH  Google Scholar 

  • Bressloff PC (2009) Stochastic neural field theory and the system-size expansion. SIAM J Appl Math 70(5):1488–1521

    MathSciNet  MATH  Google Scholar 

  • Bressloff PC (2012) Spatiotemporal dynamics of continuum neural fields. J Phys A Math Theor 45:033001

    MathSciNet  MATH  Google Scholar 

  • Bressloff PC (2014) Stochastic neural field theory. In: Coombes S, beim Graben P, Potthast R, Wright J (eds) Neural fields, theory and applications. Springer, Berlin

    Google Scholar 

  • Bressloff PC, Webber MA (2012) Front propagation in stochastic neural fields. SIAM J Appl Dyn Syst 11(2):708–740

    MathSciNet  MATH  Google Scholar 

  • Coombes S (2005) Waves, bumps, and patterns in neural field theories. Biol Cybern 93:91–108

    MathSciNet  MATH  Google Scholar 

  • Coombes S, beim Graben P, Potthast R (2014) Tutorial on neural field theory. In: Coombes S, beim Graben P, Potthast R, Wright J (eds) Neural fields, theory and applications. Springer, Berlin

    MATH  Google Scholar 

  • Crauel H, Flandoli F (1994) Attractors for random dynamical systems. Probab Theory Relat Fields 100(3):365–393

    MathSciNet  MATH  Google Scholar 

  • Da Prato G, Zabczyk J (1988) A note on semilinear stochastic equations. Differ Integral Equ 1(2):143–155

    MathSciNet  MATH  Google Scholar 

  • Da Prato G, Zabczyk J (1996) Ergodicity for infinite dimensional systems, volume 229 of london mathematical society lecture note series. Cambridge University Press, Cambridge

    Google Scholar 

  • Da Prato G, Zabczyk J (2014) Stochastic equations in infinite dimensions, volume 152 of encyclopedia of mathematics and its applications, 2nd edn. Cambridge University Press, Cambridge

    Google Scholar 

  • da Silva SH, Pereira AL (2018) A gradient flow generated by a nonlocal model of a neural field in an unbounded domain. Topol Methods Nonlinear Anal 51(2):583–598

    MathSciNet  MATH  Google Scholar 

  • Enculescu M, Bestehorn M (2007) Liapunov functional for a delayed integro-differential equation model of a neural field. Eur Phys Lett 77:68007

    Google Scholar 

  • Ermentrout GB (1998) Neural networks as spatio-temporal pattern-forming systems. Rep Prog Phys 61(4):353

    Google Scholar 

  • Ermentrout GB, Foilas SE, Kilpatrick ZP (2014) Spatiotemporal pattern formation in neural fields with linear adaptation. In: Coombes S, beim Graben P, Potthast R, Wright J (eds) Neural fields, theory and applications. Springer, Berlin

    Google Scholar 

  • Es-Sarhir A, Stannat W (2008) Invariant measures for semilinear SPDE’s with local Lipschitz drift coefficients and applications. J Evol Equ 8(1):129–154

    MathSciNet  MATH  Google Scholar 

  • Es-Sarhir A, Scheutzow M, Tölle JM, van Gaans O (2013) Invariant measures for monotone SPDEs with multiplicative noise term. Appl Math Optim 68(2):275–287

    MathSciNet  MATH  Google Scholar 

  • Faugeras O, Inglis J (2015) Stochastic neural field equations: a rigorous footing. J Math Biol 71(2):259–300

    MathSciNet  MATH  Google Scholar 

  • Ferreira JC, Menegatto VA (2009) Eigenvalues of integral operators defined by smooth positive definite kernels. Integral Equ Oper Theory 64(1):61–81

    MathSciNet  MATH  Google Scholar 

  • Ferreira JC, Menegatto VA (2013) Positive definiteness, reproducing kernel Hilbert spaces and beyond. Ann Funct Anal 4(1):64–88

    MathSciNet  MATH  Google Scholar 

  • Ferreira JC, Menegatto VA, Oliveira CP (2008) On the nuclearity of integral operators. Positivity 13(3):519–541

    MathSciNet  MATH  Google Scholar 

  • Gess B, Tölle JM (2014) Multi-valued, singular stochastic evolution inclusions. J Math Pures Appl 101(6):789–827

    MathSciNet  MATH  Google Scholar 

  • Gourley SA (2000) Travelling front solutions of a nonlocal Fisher equation. J Math Biol 41(3):272–284

    MathSciNet  MATH  Google Scholar 

  • Hagen R, Roch S, Silbermann B (2001) \(C^{\ast }\)-algebras and numerical analysis. Marcel Dekker, New York

    MATH  Google Scholar 

  • Hairer M, Mattingly JC (2006) Ergodicity of the 2D Navier–Stokes equations with degenerate stochastic forcing. Ann Math (2) 164(3):993–1032

    MathSciNet  MATH  Google Scholar 

  • Hairer M, Mattingly JC (2011) A theory of hypoellipticity and unique ergodicity for semilinear stochastic PDEs. Electron J Probab 16(23):658–738

    MathSciNet  MATH  Google Scholar 

  • Inglis J, MacLaurin J (2016) A general framework for stochastic traveling waves and patterns, with application to neural field equations. SIAM J Appl Dyn Syst 15(1):195–234

    MathSciNet  MATH  Google Scholar 

  • Jordan R, Kinderlehrer D, Otto F (1998) The variational formulation of the Fokker–Planck equation. SIAM J Math Anal 29(1):1–17

    MathSciNet  MATH  Google Scholar 

  • Jüngel A, Kuehn C, Trussardi L (2017) A meeting point of entropy and bifurcations in cross-diffusion herding. Eur J Appl Math 28(2):317–356

    MathSciNet  MATH  Google Scholar 

  • Kilpatrick ZP, Ermentrout B (2013) Wandering bumps in stochastic neural fields. SIAM J Appl Dyn Syst 12(1):61–94

    MathSciNet  MATH  Google Scholar 

  • Kolmogorov AN (1937) Zur Umkehrbarkeit der statistischen Naturgesetze. Math Ann 113:766–772

    MathSciNet  MATH  Google Scholar 

  • Krüger J, Stannat W (2014) Front propagation in stochastic neural fields: a rigorous mathematical framework. SIAM J Appl Dyn Syst 13(3):1293–1310

    MathSciNet  MATH  Google Scholar 

  • Krüger J, Stannat W (2017) Well-posedness of the stochastic neural field equation with discontinuous firing rate. J Evol Equ 27(12):1–33

    MATH  Google Scholar 

  • Kuehn C, Riedler MG (2014) Large deviations for nonlocal stochastic neural fields. J Math Neurosci 4(1):1–33

    MathSciNet  MATH  Google Scholar 

  • Laing CR (2014) PDE methods for two-dimensional neural fields. In: Coombes S, beim Graben P, Potthast R, Wright J (eds) Neural fields, theory and applications. Springer, Berlin

    Google Scholar 

  • Laing CR, Troy WC (2003) PDE methods for nonlocal models. SIAM J Appl Dyn Syst 2(3):487–516

    MathSciNet  MATH  Google Scholar 

  • Lang E (2016) A multiscale analysis of traveling waves in stochastic neural fields. SIAM J Appl Dyn Syst 15(3):1581–1614

    MathSciNet  MATH  Google Scholar 

  • Liu W, Röckner M (2015) Stochastic partial differential equations: an introduction. Springer, Cham Universitext

    MATH  Google Scholar 

  • Liu W, Tölle JM (2011) Existence and uniqueness of invariant measures for stochastic evolution equations with weakly dissipative drifts. Electron Commun Probab 16:447–457

    MathSciNet  MATH  Google Scholar 

  • Marcus R (1974) Parabolic Itô equations. Trans Am Math Soc 198:177–190

    MATH  Google Scholar 

  • Marcus R (1978) Parabolic Itô equations with monotone nonlinearities. J Funct Anal 29(3):275–286

    MATH  Google Scholar 

  • Maslowski B (1989) Strong Feller property for semilinear stochastic evolution equations and applications. In: Zabczyk J (ed) Stochastic systems and optimization. Proceedings of the sixth IFIP WG 7.1 working conference held in Warsaw, 12–16 September, 1988 (Lecture notes in control and information sciences), vol 136. Springer, Berlin, pp 210–224

  • Mogilner A, Edelstein-Keshet L (1999) A non-local model for a swarm. J Math Biol 38(6):534–570

    MathSciNet  MATH  Google Scholar 

  • Moreno-Bote R, Rinzel J, Rubin N (2007) Noise-induced alternations in an attractor network model of perceptual bistability. J Neurophysiol 98(3):1125–1139

    Google Scholar 

  • Mück S (1995) Semilinear stochastic equations for symmetric diffusions. Stoch Stoch Rep 62(3–4):303–325

    MathSciNet  MATH  Google Scholar 

  • Otto F (2001) The geometry of dissipative evolution equations: the porous medium equation. Commun Partial Differ Equ 26(1):101–174

    MathSciNet  MATH  Google Scholar 

  • Poll D, Kilpatrick ZP (2015) Stochastic motion of bumps in planar neural fields. SIAM J Appl Math 75(4):1553–1577

    MathSciNet  MATH  Google Scholar 

  • Reed M, Simon B (1980) Methods of modern mathematical physics I. Functional analysis. Academic Press, New York revised and enlarged edition

    MATH  Google Scholar 

  • Ren J, Röckner M, Wang F-Y (2007) Stochastic generalized porous media and fast diffusion equations. J Differ Equ 238(1):118–152

    MathSciNet  MATH  Google Scholar 

  • Riedler MG, Buckwar E (2013) Laws of large numbers and Langevin approximations for stochastic neural field equations. J Math Neurosci 3(1):1

    MathSciNet  MATH  Google Scholar 

  • Röckner M, Wang F-Y (2008) Non-monotone stochastic generalized porous media equations. J Differ Equ 245(12):3898–3935

    MathSciNet  MATH  Google Scholar 

  • Sasvári Z (2013) Multivariate characteristic and correlation functions. De Gruyter, Berlin

    MATH  Google Scholar 

  • Schwalger T, Deger M, Gerstner W (2017) Towards a theory of cortical columns: from spiking neurons to interacting neural populations of finite size. PLoS Comput Biol 13(4):e1005507

    Google Scholar 

  • Showalter RE (1997) Monotone operators in Banach space and nonlinear partial differential equations. Mathematical surveys and monographs. American Mathematical Society, Rhode Island

    Google Scholar 

  • Shriki O, Hansel D, Sompolinsky H (2003) Rate models for conductance-based cortical neuronal networks. Neural Comput 15:1809–1841

    MATH  Google Scholar 

  • Stewart J (1976) Positive definite functions and generalizations, an historical survey. Rocky Mt J Math 6(3):409–434

    MathSciNet  MATH  Google Scholar 

  • Topaz CM, Bertozzi AL, Lewis MA (2006) A nonlocal continuum model for biological aggregation. Bull Math Biol 68(7):1601

    MathSciNet  MATH  Google Scholar 

  • Touboul J, Hermann G, Faugeras O (2012) Noise-induced behaviors in neural mean field dynamics. SIAM J Appl Dyn Syst 11(1):49–81

    MathSciNet  MATH  Google Scholar 

  • Tricomi F (1985) Integral equations. Pure and applied mathematics, vol 5. Dover Publications, New York

    Google Scholar 

  • van Ee R (2005) Dynamics of perceptual bi-stability for stereoscopic slant rivalry and a comparison with grating, house-face, and Necker cube rivalry. Vis Res 45:29–40

    Google Scholar 

  • Veltz R, Faugeras O (2010) Local/global analysis of the stationary solutions of some neural field equations. SIAM J Appl Dyn Syst 9(3):954–998

    MathSciNet  MATH  Google Scholar 

  • Webber MA, Bressloff PC (2013) The effects of noise on binocular rivalry waves: a stochastic neural field model. J Stat Mech 2013:P03001

    MathSciNet  Google Scholar 

  • Wilson H, Cowan J (1973) A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue. Biol Cybern 13(2):55–80

    MATH  Google Scholar 

  • Xie S, Lawniczak AT, Krishnan S, Lio P (2012) Wavelet kernel principal component analysis in noisy multiscale data classification. ISRN Comput Math 2012:197352

    MATH  Google Scholar 

  • Zabczyk J (1989) Symmetric solutions of semilinear stochastic equations. In: Da Prato G, Tubaro L (eds) Stochastic partial differential equations and applications II. Proceedings of the second conference held in Trento, 1–6 February 1988 (Lecture notes in mathematics), vol 1390. Springer, Berlin, pp 237–256

    Google Scholar 

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Correspondence to Christian Kuehn.

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Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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

$$\begin{aligned} W_{t}=\sum _{i=1}^{\infty }v_{i}\beta _{t}^{i},\quad t\ge 0, \end{aligned}$$
(A.1)

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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