In the context of minimally cognitive behavior, we used multi-robotic systems to investigate the emergence of communication and cooperation during the evolution of recurrent neural networks. The networks are systematically analyzed to identify their relevant dynamical properties. Evolution efficiently adapts these properties through small structural changes within the networks when specific environmental conditions are altered, such as the number of interacting robots. The findings signify the importance of reducing the predefined knowledge about resulting behaviors, dynamical properties of control, and the topology of neural networks in order to utilize the strength of the Evolutionary Robotics approach to Artificial Life.