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An internal model of the own body can be assumed a fundamental andevolutionary-early representation as it is present throughout the animalkingdom. Such functional models are, on the one hand, required in motorcontrol, for example solving the inverse kinematic or dynamic task ingoal-directed movements or a forward task in ballistic movements. On the otherhand, such models are recruited in cognitive tasks as are planning ahead orobservation of actions of a conspecific. Here, we present a functional internalbody model that is based on the Mean of Multiple Computations principle. Forthe first time such a model is completely realized in a recurrent neuralnetwork as necessary normalization steps are integrated into the neural modelitself. Secondly, a dynamic extension is applied to the model. It is shown howthe neural network solves a series of inverse tasks. Furthermore, emergingrepresentation in transformational layers are analyzed that show a form ofprototypical population-coding as found in place or direction cells.

Setup of a Recurrent Neural Network as a Body Model for Solving Inverse and Forward Kinematics as well as Dynamics for a Redundant Manipulator