Numerical methods are important in computational neuroscience where complex
nonlinear systems are studied. This report evaluates two methodologies,
finite differences and Fourier series, for numerically integrating a nonlinear
neural model based on a partial integro-differential equation. The stability
and convergence criteria of four finite difference methods is investigated and
their efficiency determined. Various ODE solvers in Matlab are used with the
Fourier series method to solve the neural model, with an evaluation of the
accuracy of the approximation versus the efficiency of the method. The two
methodologies are then compared
