Although robotics has made progress with respect to adaptability and interaction in natural environments, it cannot match the capabilities of biological systems. A promising approach to solve this problem is to create biologically plausible robot controllers that use detailed neuronal networks. However, this approach yields a large gap between the neuronal network and its connection to the robot on the one side and the technical implementation on the other. Existing approaches neglect bridging this gap between disciplines and their focus on different abstractions layers but manually hand-craft the simulations. This makes the tight technical integration cumbersome and error-prone impairing round-trip validation and academic advancements. Our approach maps the problem to model-driven engineering techniques and defines a domain-specific language (DSL) for integrating biologically plausible Neuronal Networks in robot control algorithms. It provides different levels of abstraction and sets an interface standard for integration. Our approach is implemented in the Neuro-Robotics Platform (NRP) of the Human Brain Project (HBP). Its practical applicability is validated in a minimalist experiment inspired by the Braitenberg vehicles based on the simulation of a four-wheeled Husky robot controlled by a neuronal network.