A simulator calculating two-dimensional dynamic neural fields with multiple order derivatives is presented in this work. The simulated neural fields are of the type ... where I, L and S are respectively a field's input, spatial delay kernel with axonal transmission speed c and nonlinear firing rate function S = S0 / (1 + exp(-α(V-Θ)). A Fast Fourier Transform in space is used to accelerate the integral calculation. The stochastic differential equation is useful for studying processes such as breathers, activity spread and Turing patterns shown below. The underlying code is written in Python and is therefore cross-platform. It is also open-source, allowing researchers to modify the simulator in any manner.