Numerical approximation of the time fractional cable model arising in neuronal dynamics | Engineering with Computers Skip to main content
Log in

Numerical approximation of the time fractional cable model arising in neuronal dynamics

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

Abstract

The cable equation is one useful description for modeling phenomena such as neuronal dynamics and electrophysiology. The time-fractional cable model (TFCM) generalizes the classical cable equation by considering the anomalous diffusion that occurs in the ionic motion present for example in the neuronal system. This paper proposes a novel meshless numerical procedure, the radial basis function-generated finite difference (RBF-FD), to approximate the TFCM involving two fractional temporal derivatives. The time discretization of the TFCM is performed based on the Grünwald–Letnikov expansion. The spatial derivatives are discretized using the RBF-FD. The pattern of data distribution in the support domain is assumed as having a fixed number of points. The RBF-FD is based on the local support domain that leads to a sparsity system and also tackles the ill-conditioning problem caused by global collocation method. The theoretical stability and the convergence analysis of the scheme are also discussed in detail. It is shown that the proposed method is efficient and that the numerical results confirm the theoretical formulation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Baleanu D, Güvenç ZB, Machado JT et al (2010) New trends in nanotechnology and fractional calculus applications. Springer, Berlin

    MATH  Google Scholar 

  2. Baleanu D, Machado JAT, Luo AC (2011) Fractional dynamics and control. Springer, Berlin

    Google Scholar 

  3. Barkai E, Metzler R, Klafter J (2000) From continuous time random walks to the fractional Fokker–Planck equation. Phys Rev E 61(1):132

    MathSciNet  Google Scholar 

  4. Buhmann MD (2003) Radial basis functions: theory and implementations, vol 12. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  5. Bulut F, Oruç Ö, Esen A (2015) Numerical solutions of fractional system of partial differential equations by Haar wavelets. Comput Model Eng Sci 108(4):263–284

    Google Scholar 

  6. Cavlak E, Bayram M et al (2014) An approximate solution of fractional cable equation by homotopy analysis method. Bound Value Probl 2014(1):58

    MathSciNet  MATH  Google Scholar 

  7. Dehestani H, Ordokhani Y, Razzaghi M (2019) Application of the modified operational matrices in multiterm variable-order time-fractional partial differential equations. Math Methods Appl Sci 42(18):7296–7313

    MathSciNet  MATH  Google Scholar 

  8. Dehghan M, Abbaszadeh M (2016) Analysis of the element free Galerkin (EFG) method for solving fractional cable equation with dirichlet boundary condition. Appl Numer Math 109:208–234

    MathSciNet  MATH  Google Scholar 

  9. Dentz M, Cortis A, Scher H, Berkowitz B (2004) Time behavior of solute transport in heterogeneous media: transition from anomalous to normal transport. Adv Water Resour 27(2):155–173

    Google Scholar 

  10. Esen A, Bulut F, Oruç Ö (2016) A unified approach for the numerical solution of time fractional Burgers’ type equations. Eur Phys J Plus 131(4):116

    Google Scholar 

  11. Fasshauer GE (2007) Meshfree approximation methods with matlab: (with CD-ROM), vol 6. World Scientific Publishing Company, Singapore

    MATH  Google Scholar 

  12. Franke C, Schaback R (1998) Convergence order estimates of meshless collocation methods using radial basis functions. Adv Comput Math 8(4):381–399

    MathSciNet  MATH  Google Scholar 

  13. Franke R (1982) Scattered data interpolation: tests of some methods. Math Comput 38(157):181–200

    MathSciNet  MATH  Google Scholar 

  14. Hardy RL (1971) Multiquadric equations of topography and other irregular surfaces. J Geophys Res 76(8):1905–1915

    Google Scholar 

  15. Hardy RL (1990) Theory and applications of the multiquadric-biharmonic method 20 years of discovery 1968–1988. Comput Math Appl 19(8–9):163–208

    MathSciNet  MATH  Google Scholar 

  16. Kansa EJ (1990) Multiquadrics—a scattered data approximation scheme with applications to computational fluid-dynamics—I surface approximations and partial derivative estimates. Comput Math Appl 19(8–9):127–145

    MathSciNet  MATH  Google Scholar 

  17. Kansa EJ (1990) Multiquadrics—a scattered data approximation scheme with applications to computational fluid-dynamics—II solutions to parabolic, hyperbolic and elliptic partial differential equations. Comput Math Appl 19(8–9):147–161

    MathSciNet  MATH  Google Scholar 

  18. Koch C (1999) Biophysics of computation: information processing in single neurons. Oxford University Press, Oxford

    Google Scholar 

  19. Langlands T, Henry B, Wearne S (2005) Solution of a fractional cable equation: finite case. Applied mathematics report AMR05/34, University of New South Wales

  20. Langlands T, Henry B, Wearne S (2009) Fractional cable equation models for anomalous electrodiffusion in nerve cells: infinite domain solutions. J Math Biol 59(6):761

    MathSciNet  MATH  Google Scholar 

  21. Lin Y, Li X, Xu C (2011) Finite difference/spectral approximations for the fractional cable equation. Math Comput 80(275):1369–1396

    MathSciNet  MATH  Google Scholar 

  22. Liu F, Yang Q, Turner I (2011) Two new implicit numerical methods for the fractional cable equation. J Comput Nonlinear Dyn 6(1):011009

    Google Scholar 

  23. Liu F, Zhuang P, Anh V, Turner I, Burrage K (2007) Stability and convergence of the difference methods for the space-time fractional advection–diffusion equation. Appl Math Comput 191(1):12–20

    MathSciNet  MATH  Google Scholar 

  24. Machado J (1998) Fractional-order derivative approximations in discrete-time control systems. SAMS, Carmel, pp 1–16

    Google Scholar 

  25. Madych W, Nelson S (1990) Multivariate interpolation and conditionally positive definite functions. II. Math Comput 54(189):211–230

    MathSciNet  MATH  Google Scholar 

  26. Metzler R, Klafter J (2000) The random walk’s guide to anomalous diffusion: a fractional dynamics approach. Phys Rep 339(1):1–77

    MathSciNet  MATH  Google Scholar 

  27. Metzler R, Klafter J, Sokolov IM (1998) Anomalous transport in external fields: continuous time random walks and fractional diffusion equations extended. Phys Rev E 58(2):1621

    Google Scholar 

  28. Micchelli CA (1984) Interpolation of scattered data: distance matrices and conditionally positive definite functions. In: Singh SP, Burry JWH, Watson B (eds) Approximation theory and spline functions. Springer, Dordrecht, pp 143–145

    Google Scholar 

  29. Milici C, Drăgănescu G, Machado JT (2019) Introduction to fractional differential equations, vol 25. Springer, Dordrecht

    MATH  Google Scholar 

  30. Nikan O, Golbabai A, Machado JT, Nikazad T (2020) Numerical solution of the fractional Rayleigh–Stokes model arising in a heated generalized second-grade fluid. Eng Comput., pp 1–14. https://doi.org/10.1007/s00366-019-00913-y

    Chapter  Google Scholar 

  31. Nikan O, Machado JT, Golbabai A, Nikazad T (2019) Numerical investigation of the nonlinear modified anomalous diffusion process. Nonlinear Dyn 97(4):2757–2775

    MATH  Google Scholar 

  32. Nikan O, Machado JT, Golbabai A, Nikazad T (2020) Numerical approach for modeling fractal mobile/immobile transport model in porous and fractured media. Int Commun Heat Mass Transfer 111:104443

    Google Scholar 

  33. Oldham KB, Spanier J (1974) The fractional calculus, vol 111 of mathematics in science and engineering. Elsevier, Amsterdam

    MATH  Google Scholar 

  34. Oruç Ö, Esen A, Bulut F (2019) A haar wavelet approximation for two-dimensional time fractional reaction–subdiffusion equation. Eng Comput 35(1):75–86

    Google Scholar 

  35. Oruç Ö, Esen A, Bulut F (2019) A unified finite difference Chebyshev wavelet method for numerically solving time fractional Burgers’ equation. Discrete Contin Dyn Syst S 12(3):533

    MathSciNet  MATH  Google Scholar 

  36. Podlubny I (1998) Fractional differential equations: an introduction to fractional derivatives, fractional differential equations, to methods of their solution and some of their applications, vol 198. Elsevier, Amsterdam

    MATH  Google Scholar 

  37. Qian N, Sejnowski T (1989) An electro-diffusion model for computing membrane potentials and ionic concentrations in branching dendrites, spines and axons. Biol Cybern 62(1):1–15

    MATH  Google Scholar 

  38. Quintana-Murillo J, Yuste SB (2011) An explicit numerical method for the fractional cable equation. Int J Differ Equ 2011:231920

    MathSciNet  MATH  Google Scholar 

  39. Rahimkhani P, Ordokhani Y (2019) A numerical scheme based on Bernoulli wavelets and collocation method for solving fractional partial differential equations with dirichlet boundary conditions. Numer Methods Partial Differ Equ 35(1):34–59

    MathSciNet  MATH  Google Scholar 

  40. Rahimkhani P, Ordokhani Y (2020) Approximate solution of nonlinear fractional integro-differential equations using fractional alternative Legendre functions. J Comput Appl Math 365:112365

    MathSciNet  MATH  Google Scholar 

  41. Ritchie K, Shan XY, Kondo J, Iwasawa K, Fujiwara T, Kusumi A (2005) Detection of non-Brownian diffusion in the cell membrane in single molecule tracking. Biophys J 88(3):2266–2277

    Google Scholar 

  42. Sarra SA (2012) A local radial basis function method for advection–diffusion–reaction equations on complexly shaped domains. Appl Math Comput 218(19):9853–9865

    MathSciNet  MATH  Google Scholar 

  43. Sarra SA, Kansa EJ (2009) Multiquadric radial basis function approximation methods for the numerical solution of partial differential equations. Adv Comput Mech 2(2):1–206

    Google Scholar 

  44. Saxton MJ (2001) Anomalous subdiffusion in fluorescence photobleaching recovery: a Monte Carlo study. Biophys J 81(4):2226–2240

    Google Scholar 

  45. Schaback R (1995) Error estimates and condition numbers for radial basis function interpolation. Adv Comput Math 3(3):251–264

    MathSciNet  MATH  Google Scholar 

  46. Shivanian E, Jafarabadi A (2018) An improved meshless algorithm for a kind of fractional cable problem with error estimate. Chaos Solitons Fractals 110:138–151

    MathSciNet  MATH  Google Scholar 

  47. Shu C, Ding H, Yeo K (2003) Local radial basis function-based differential quadrature method and its application to solve two-dimensional incompressible Navier–Stokes equations. Comput Methods Appl Mech Eng 192(7–8):941–954

    MATH  Google Scholar 

  48. Tolstykh A, Shirobokov D (2003) On using radial basis functions in a “finite difference mode” with applications to elasticity problems. Comput Mech 33(1):68–79

    MathSciNet  MATH  Google Scholar 

  49. Uchaikin VV (2013) Fractional derivatives for physicists and engineers, vol 2. Springer, Berlin

    MATH  Google Scholar 

  50. Wendland H (2004) Scattered data approximation, vol 17. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  51. Wright GB, Fornberg B (2006) Scattered node compact finite difference-type formulas generated from radial basis functions. J Comput Phys 212(1):99–123

    MathSciNet  MATH  Google Scholar 

  52. Yuste SB (2006) Weighted average finite difference methods for fractional diffusion equations. J Comput Phys 216(1):264–274

    MathSciNet  MATH  Google Scholar 

  53. Zhang H, Yang X, Han X (2014) Discrete-time orthogonal spline collocation method with application to two-dimensional fractional cable equation. Comput Math Appl 68(12):1710–1722

    MathSciNet  MATH  Google Scholar 

  54. Zhuang P, Liu F, Turner I, Anh V (2016) Galerkin finite element method and error analysis for the fractional cable equation. Numer Algorithms 72(2):447–466

    MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

The authors express their gratitude to the Editor-in-Chief (Professor Mark Shephard), Associate Editor and both the reviewers for their valuable suggestions and useful comments to make this paper better.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to O. Nikan.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nikan, O., Golbabai, A., Machado, J.A.T. et al. Numerical approximation of the time fractional cable model arising in neuronal dynamics. Engineering with Computers 38, 155–173 (2022). https://doi.org/10.1007/s00366-020-01033-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-020-01033-8

Keywords

Mathematics Subject Classification

Navigation