Neural Network Model of Spatial Memory: Associative Recall of Maps

Abstract

This paper offers a neural network model that can memorize and recall spatial maps. When driving through a place we have been before, we can recall and imagine the scenery that we cannot see yet but shall see soon. Triggered by the newly recalled image, we can also recall other scenery further ahead of us. The model emulates such a chain process of recalling using a correlation matrix memory. A correlation matrix memory by itself, however, does not accept shifts in location of stimulus patterns, and each stimulus pattern has to be placed accurately at the location of one of the memorized patterns. We propose adjusting the location of the stimulus pattern using the cross-correlation between the stimulus pattern and the “piled pattern”, which is the sum of all patterns memorized in the correlation matrix. A map of Europe is divided into a number of overlapping segments, and these segments are memorized in the proposed model. Triggered by an input image, say a map around Scotland, the model can recall maps of other parts of Europe sequentially up to Italy, for example. © 1997 Elsevier Science Ltd.