Bidirectional associative memories
IEEE Transactions on Systems, Man and Cybernetics
Methods in Neuronal Modeling: From Ions to Networks
Methods in Neuronal Modeling: From Ions to Networks
Perceptrons: An Introduction to Computational Geometry
Perceptrons: An Introduction to Computational Geometry
Optimal matrix compression yields storage capacity 1 for binary Willshaw associative memory
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
IEEE Transactions on Neural Networks
A cell assembly based model for the cortical microcircuitry
Neurocomputing
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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This work concisely reviews and unifies the analysis of different variants of neural associative networks consisting of binary neurons and synapses (Willshaw model). We compute storage capacity, fault tolerance, and retrieval efficiency and point out problems of the classical Willshaw model such as limited fault tolerance and restriction to logarithmically sparse random patterns. Then we suggest possible solutions employing spiking neurons, compression of the memory structures, and additional cell layers. Finally, we discuss from a technical perspective whether distributed neural associative memories have any practical advantage over localized storage, e.g., in compressed look-up tables.