Using evolutionary simulations, we develop autonomous agents controlled by artificial neural networks (ANNs). In simple life-like tasks of foraging and navigation, high performance levels are attained by agents equipped with fully-recurrent ANN controllers. Examining several experimental settings, differing in the sensory input available to the agents, we find a common structure of a “command neuron” switching the dynamics of the network between radically different behavioural modes. In some of the models the command neuron reflects a map of the environment, acting as a “place cell”. In others it is based on a spontaneously evolving short-term memory mechanism. The resemblance to known findings from neurobiology places Evolved ANNs as an excellent candidate model for the study of structure and function relation in complex nervous systems.