First, since the size of the models employed here is far from the thermodynamic limit, instead of directly using the diverging asymptotic form c(r) ∝ 1/rη, we approximate it by computing the correlation structure of a finite model operating at a known critical temperature. One of the few cases where the Ising model presents an exact solution is a model with zero fields and a bidimensional square lattice connectivity, in which a critical point appears at \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$J={\rm{l}}{\rm{o}}{\rm{g}}\,(1+\sqrt{2})/(2\beta )$$\end{document}J=log(1+2)/(2β)19. Exploiting this, we build a 20 × 20 square lattice Ising model operating at critical temperature with periodic boundary conditions to generate reference correlations to be used by the learning rule. We simulate the model using Glauber Dynamics, generating 106 samples, after an initial run of 105 updates from a random state. From this simulation, we obtain the distribution of correlations in the system P(cij), where cij = 〈sisj〉, observed in Fig. 1. Since the fields hi of all units are zero, the means mi = 〈si〉 of all units are also zero.Figure 1Distribution of correlation values used for learning, generated from a 20 × 20 lattice Ising model at critical temperature.