Example for depth potential function as described in Boucher et al, MedIA 2009

Hello,

The main contribution of this PR is related to the implementation of an example provided in Depth potential function for folding pattern representation, registration and analysis, Maxime Boucher, Sue Whitesides, Alan Evans, Medical Image Analysis, 2009 :

Define a parametric surface depending on two parameters M and sigma. This surface is a second-order derivative in y direction with standard deviation sigma) and a gaussian in x direction. Amplitude is encoded by M. So it can be seen as schematic representation for a cortical fold.
Compute the depth potential function by using Laplacian discretization and Rusinkiewicz curvature as proposed in brain-slam
Make vary the parameter alpha used to compute the DPF and look at the evolution of the DPF at the center (negative peak) and at the secondary peaks
Make vary M and look at the DPF.

Other contributions:

add a parameter to change the background color in visbrain_plot ("black" by default)
small change in differential_geometry/depth_potential_function (factor 2 as in the Boucher paper)
pep8 check for all the .py with flake8

brain-slam / slam

Example for depth potential function as described in Boucher et al, MedIA 2009 #71