To sort through the conflicting claims, we extended and analysed the most prominent of the previously proposed models and compared their predictions with the experimental data. For convenience we grouped the major models into classes and types based on similarities in goals or implementation. The two main classes are pattern models and developmental models. Pattern models attempt to provide a characterization of map patterns in schematic drawings or simple equations. They include both structural and spectral models.
Developmental models give more attention to proposed mechanisms of pattern formation.
Their pattern-generating equations are meant to correspond more closely to actual physiological processes. Developmental models include correlation-based learning, competitive Hebbian models, and several others. This study revealed that several pattern models, as well as an entire class of developmental correlation-based learning models, are incompatible with experimental data, whereas competitive Hebbian models and several particular pattern models are in accordance with the experimental findings. Furthermore, we found that despite apparent differences, most models are based on similar principles and consequently make similar predictions. Among the pattern models, the spectral models perform better than the previously proposed structural models, mainly because they account for global disorder and for the coexistence of linear zones and singularities.