However, instead of restricting a model to a particular set of mechanisms or a given topology, we decide to design a learning rule that preserves a distribution of correlations of a critical point belonging to a specific universality class. This could capture some of the properties of that universality class without choosing a specific topology or parametrical configuration. The next goal would be to test whether this adaptive mechanism has the capability of driving an arbitrary system to a critical point. And, in case it were so, the system poised near a critical point should display interesting features of adaptive behaviour as maximal sensitivity or a wide dynamic range of behaviours.