Separating visual information into position and direction by SOM

Abstract

A model is proposed to self-organize a map for the visual recognition of position and direction by a robot moving autonomously in a room. The robot is assumed to have visual sensors. The model is based on Kohonen’s self-organizing map (SOM), which was proposed as a model of self-organization of the cortex. An ordinary SOM consists of a two-dimensional array of neuron-like feature detector units. In our model, however, units are arranged in a three-dimensional array, and a periodic boundary condition is assumed in one dimension. Also, some new learning rules are added. Our model is shown by a computer simulation to form a map which can extract from the visual input two factors of information separately, i.e., the position and direction of the robot. This is an example of so-called two-factor problems. In our algorithm, the difference in the topology of the information is used to separate two factors of information.