A Simple Selforganizing Neural Network Architecture for Selective Visual Attention

Abstract

We present a simple neural network architecture which autonomously learns how to control a data driven selective attention process. In order to control the selective attention process a biologically plausible position coding is used which leads to fuzzy representations of position. An associative memory learns the connections between subsequent positions und local features. The result of presenting simple Real-World color images to the neural network architecture is shown.