  An internal model of the own body can be assumed a fundamental and
evolutionary-early representation as it is present throughout the animal
kingdom. Such functional models are, on the one hand, required in motor
control, for example solving the inverse kinematic or dynamic task in
goal-directed movements or a forward task in ballistic movements. On the other
hand, such models are recruited in cognitive tasks as are planning ahead or
observation of actions of a conspecific. Here, we present a functional internal
body model that is based on the Mean of Multiple Computations principle. For
the first time such a model is completely realized in a recurrent neural
network as necessary normalization steps are integrated into the neural model
itself. Secondly, a dynamic extension is applied to the model. It is shown how
the neural network solves a series of inverse tasks. Furthermore, emerging
representation in transformational layers are analyzed that show a form of
prototypical population-coding as found in place or direction cells.
