In this chapter we will describe a developmental form of Cartesian Genetic Programming (CGP) known as a CGP Developmental Network (CGPDN). The CGPDN is a kind of constructivist artificial neural network in which the neuron is represented by seven evolved CGP programs. These programs are each responsible for some neuro-inspired aspect of the artificial neuron (i.e. soma, dendrites, axons, synapses and neurite branches). The network is usually initialized with a few neurons. However, when the evolved programs are executed the network can develop into a network of arbitrary complexity while simultaneously solving a computational problem. We have tested this model on two well known problem in artificial intelligence: Wumpus World and Checkers (Draughts). The role of CGP is to evolve programs that encode the capability of learning, rather than learned information directly. All specific learned information is acquired post-evolution while solving problems.