Neuronal network modeling and experiments demonstrate concordant variations of neuronal parameters that preserve functional characteristics of network activity. Finding such concordant variations advances our understanding on how the properties of individual neurons determine network functionality, and show possible paths for homeostatic regulation. In the talk, I will present a new general approach for finding and analyzing concordant neuronal variations that do not change network output. The approach is based on the evaluation of local interrelations between neuronal parameters that did not change the network output.