High-Order Neural Networks: Information Storage without Errors
L. Personnaz, Isabelle Guyon, Gérard Dreyfus · Europhysics Letters (EPL) · 1987
A new learning rule is derived, which allows the perfect storage and the retrieval of information and sequences, in neural networks exhibiting high-order interactions between some or all neurons. Such interactions increase the storage capacity of the networks and allow to solve a class of problems which were intractable with standard networks. We show that it is possible to restrict the amount of high-order interactions while improving the attractivity of the stored patterns.