In order to test this idea we design a simple learning rule to adjust the parameters of an arbitrary Ising model to the desired distribution of correlations. In a nutshell, the learning rule will operate as follows: (1) The distribution of correlations of a finite square lattice Ising model is calculated, (2) the new model is defined by assigning each neuron reference correlation values for its synapses randomly sampled from the previous distribution, (3) during learning, each neuron sorts its synapses by their correlation strength, and adjusts these correlations to the reference values assigned using an inverse Ising learning rule. We proceed now to explain these points in detail.