Numerical Approximation for Nonlinear Noisy Leaky Integrate-and-Fire Neuronal Model

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Sharma D., Singh P., Agarwal R. P., Köksal M. E.

MATHEMATICS, vol.7, no.4, 2019 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 7 Issue: 4
  • Publication Date: 2019
  • Doi Number: 10.3390/math7040363
  • Journal Name: MATHEMATICS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Keywords: neuronal variability, Fokker-Planck-Kolmogorov equations, Galerkin finite element method, FOKKER-PLANCK EQUATION, FINITE-ELEMENT-METHOD, NETWORKS, POPULATIONS, DYNAMICS
  • Ondokuz Mayıs University Affiliated: Yes


We consider a noisy leaky integrate-and-fire (NLIF) neuron model. The resulting nonlinear time-dependent partial differential equation (PDE) is a Fokker-Planck Equation (FPE) which describes the evolution of the probability density. The finite element method (FEM) has been proposed to solve the governing PDE. In the realistic neural network, the irregular space is always determined. Thus, FEM can be used to tackle those situations whereas other numerical schemes are restricted to the problems with only a finite regular space. The stability of the proposed scheme is also discussed. A comparison with the existing Weighted Essentially Non-Oscillatory (WENO) finite difference approximation is also provided. The numerical results reveal that FEM may be a better scheme for the solution of such types of model problems. The numerical scheme also reduces computational time in comparison with time required by other schemes.