Storages Properties of Randomly Connected Boolean Neural Networks for Associative Memory
K. Y. Michael Wong, David C. Sherrington · Europhysics Letters (EPL) · 1988
Aleksander has recently proposed neural networks which replace the connection weights of conventional models by logical devices, or Boolean functions, and achieve learning by a "training with noise" algorithm. We study the statistical dynamical properties of a randomly connected Aleksander network for associative memory. We determine the retrieval error, radius of attraction and storage capacity for the case of large but dilute connectivity.