title: Self-organizing neural projections.
abstract: The Self-Organizing Map (SOM) algorithm was developed for the creation of abstract-feature maps. It has been accepted widely as a data-mining tool, and the principle underlying it may also explain how the feature maps of the brain are formed. However, it is not correct to use this algorithm for a model of pointwise neural projections such as the somatotopic maps or the maps of the visual field, first of all, because the SOM does not transfer signal patterns: the winner-take-all function at its output only defines a singular response. Neither can the original SOM produce superimposed responses to superimposed stimulus patterns. This presentation introduces a new self-organizing system model related to the SOM that has a linear transfer function for patterns and combinations of patterns all the time. Starting from a randomly interconnected pair of neural layers, and using random mixtures of patterns for training, it creates a pointwise-ordered projection from the input layer to the output layer. If the input layer consists of feature detectors, the output layer forms a feature map of the inputs.
authors.affiliation: Helsinki University of Technology, Neural Networks Research Centre, P.O. Box 5400, FI-02015 HUT, Finland. teuvo.kohonen@hut.fi
authors.forename: Teuvo
authors.initials: T
authors.lastname: Kohonen
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*