In this work we study the dynamics of random threshold networks with both excitatory and inhibitory links. We find the balance between excitatory and inhibitory links to be a key parameter in the dynamics. By varying the fraction of excitatory links, F+, we are able to control the stable activity A∞ of the network. We also analyze the influence of the average degree K on A∞, and conclude that the dynamics is independent of degree distribution for high K. We develop a mean-field approximation of the dynamics, to provide a practical tool. In the second part of this work, we propose a minimal model of an adaptive threshold network. With it, we are able to control the stable activity of a network, and of individual groups within the same network. This adaptive model can be extended in order to generate networks with controllable activity and specific topologies.