The theory of Self-organized criticality (SOC) applied in neural networks has been widely studied over the past fifteen years and its assumptions have been confirmed in both experimental and theoretical studies. The first work in this area, conducted by Beggs and Plenz, has proved that electrical activity of a population of interconnected neurons can be seen, in regular neurophysiological conditions, as a critical branching process with a branching parameter close to 1 and whose observables follow power law distributions having exponents around -3/2; divergences from such conditions are hallmarks of subcritical or supercritical working states. These and many more resultshave sustained the hypothesis that the brain operates in a self-organized critical state, that is, there may exist some still unclear phenomena that drive a healthy neural network to work at criticality.