Recapitulating the main ideas presented so far, we have tested how, by taking a set of correlations chosen at random from a distribution generated by a lattice Ising model at a critical point, we can construct a new model that appears to be also near a critical point in its parameter space. Moreover, if an embodied agent maintains these correlations using a simple learning rule while interacting with its environment–as a sort of organizational homeostasis–the agent neural controller seems to be driven to a critical point, which coincides with behavioural transitions in the agent-environment parameter space. Due to computational limitations for estimating the probability distribution of the embodied Ising networks, we only calculate the entropy and heat capacity of neural controllers up to Nh = 16 hidden neurons (and N = 22 total neurons) and approximate larger sizes computing the entropy and heat capacity of Ns = 4 sensor neurons for neural controllers up to N = 70 total neurons. These results are still far from the thermodynamic limit and further tests should confirm the results presented here for larger sizes. Nevertheless, in all cases we observe a clear diverging tendency of the heat capacity every time the size of the system is doubled. Tests for larger sizes could be performed by designing environments that can be described by a Gibbs distribution, avoiding the combinatorial explosion in computing the heat capacity of the system by calculating it directly from the energy of the whole agent-environment system.