This project is an attempt to develop a unified theoretical framework for models of neural map formation. The system self-organizes an ordered connectivity between the layers based on induced correlations.
Typical examples are retinotopic maps, occular dominance, and orientation columns.
But it also includes dynamic link matching as a special form of neural map formation. The focus of the analysis is on formal aspects. It turns out that the formulation of a system as a constrained optimization problem is particularly convenient and allows us to relate many different models. Growth rules can be derived from objective functions as gradient flows. Normalization rules can be derived from constraints by various methods, e.g. Lagrangian multipliers.