In this paper, we present a spiking neuronal model that learns to perform a motor control task. Since the long-term goal of this project is the application of such a neuronal model to study the mutual adaptation between a Brain-Computer Interface (BCI) and its user, neurobiological plausibility of the model is a key aspect. Therefore, the model was trained using reinforcement learning similar to that of the dopamine system, in which a global reward and punishment signal controlled spike-timing dependent plasticity (STDP). Based on this method, the majority of the randomly generated models were able to learn the motor control task. Although the models were only trained on two targets, they were able to reach arbitrary targets after learning. By introducing structural synaptic plasticity (SSP), which dynamically restructures the connections between neurons, the number of models that successfully learned the task could be significantly improved.