Hierarchical Associative Memories: The Neural Network for Prediction in Spatial Maps

Abstract

Techniques for prediction in spatial maps can be based on associative neural network models. In order to overcome associative memory limitations imposed by processing of a large number of mutually correlated spatial patterns, we have designed the Hierarchical Associative Memory model which consists of arbitrary number of associative memories hierarchically grouped into several layers.

In order to further improve its recall abilities, we have proposed new modification of our model. In this paper, we also present experimental results focused on recall ability of designed model and their analysis by means of mathematical statistics.