A Neural Network Model for Self-Organization of Spatial Maps

Abstract

Psychological findings suggest that humans keep spatial relation between objects in memory as ‘cognitive maps’, and that three-dimensional spatial information is represented by a set of two-dimensional information in the brain. Based on these findings, we propose a neural network model that forms a two-dimensional spatial map self-organizingly. The model is mainly composed of two functionally different parts. One represents spatial position of objects by using signals from saccadic eye movements. The other represents relative position of objects existing simultaneously in a visual field. This model was simulated on a computer to be shown to have the desired behavior.