Storing and Retrieving Information in a Layered Spin System
Eytan Domany, Ron Meir, Wolfgang Kinzel · Europhysics Letters (EPL) · 1986
We introduce a neural network model with layered architecture and binary (spin) variables. Hebbian rules are used to define unidirectional couplings between spins of adjacent layers. A fast learning algorithm produces couplings that store a large number of random patterns, and efficiently recognizes noisy patterns. Performance of this network is compared with spin-glass type models of pattern recognition.