The spatial semantic hierarchy
Artificial Intelligence
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
Synaptic depression enlarges basin of attraction
Neurocomputing
Hierarchical associative memories: the neural network for prediction in spatial maps
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
On the complexity of hierarchical associative memories
Proceedings of the 2009 ACM symposium on Applied Computing
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The strategies for an associative recall can be based on associative memory models. However, the performance of standard associative memories is very sensitive to the number of stored patterns and their mutual correlations. With respect to huge amounts of spatial patterns (mostly correlated) to be processed, we have focused on an arbitrary number of associative memories grouped into several layers (Hierarchical Associative Memories – HAM). In the newly presented HAM2-model, the patterns are hierarchically grouped according to the “previous-layer” patterns. The HAM2-model uses the information recalled by the “previous-layer” to find an appropriate subset of “next-level” associative memories. To evaluate the performance of the HAM2-model, extensive simulations are carried out. The experimental results show the recall ability of the model in the area of associative pattern recall.