Methods are described to unfold and flatten the curved, convoluted
surfaces of the brain in order to study the functional architectures
and neural maps embedded in them. In order to do this, it is necessary
to solve the general mapmaker's problem for representing curved
surfaces by planar models. This algorithm has applications in areas
other than computer-aided neuroanatomy, such as robotics motion
planning and geophysics. The algorithm maximizes the goodness of fit
distances in these surfaces to distances in a planar configuration of
points. It is illustrated with a flattening of monkey visual cortex,
which is an extremely complex folded surface. Distance errors in the
range of several percent are found, with isolated regions of larger
error, for the class of cortical surfaces studied so far.