Rigorous results for the Hopfield-type neural networks

Usually, the learning consists in repeated showings of a standard. Inevitably, the showings are accompanied by distortions of the standard. How does the result of the learning depend on these distortions? For the Hopfield-type network we solve the problem analytically in the case of the memorized patterns are the distorted copies of the same standard.