A Neural Network Model for Spatial Information Representation

In this paper, we propose a neural network model that forms a two-dimensional spatial relation map self-organizingly. Cues for spatial relations between objects are given by efference copy signals of saccadic eye movements. The model is able to code the relative positions of objects existing simultaneously in the visual field in spite of its simple structure. The model was simulated on a computer to be shown to have the desired behavior.