  Self-Organizing Maps are models for unsupervised representation formation of
cortical receptor fields by stimuli-driven self-organization in laterally
coupled winner-take-all feedforward structures. This paper discusses
modifications of the original Kohonen model that were motivated by a potential
function, in their ability to set up a neural mapping of maximal mutual
information. Enhancing the winner update, instead of relaxing it, results in an
algorithm that generates an infomax map corresponding to magnification exponent
of one. Despite there may be more than one algorithm showing the same
magnification exponent, the magnification law is an experimentally accessible
quantity and therefore suitable for quantitative description of neural
optimization principles.
