To our understanding, modelling the dynamics of brain functions on cell level is essential to develop both a deeper understanding and classification of the experimental data as well as a guideline for further research. This paper now presents the implementation and training of a direction sensitive network on the basis of a biophisical neurone model including synaptic excitation, dendritic propagation and action-potential generation. The underlying model not only describes the functional aspects of neural signal processing, but also provides insight into their underlying energy consumption. Moreover, the training data set has been recorded by means of a real robotics system, thus bridging the gap to technical applications.