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

## TL;DR

This paper introduces a recurrent neural network-based internal body model capable of solving inverse and forward kinematics and dynamics for a redundant manipulator, integrating normalization and dynamic extensions.

## Contribution

It presents the first complete realization of a neural body model based on the Mean of Multiple Computations principle, including normalization and dynamic capabilities.

## Key findings

- The neural network successfully solves inverse kinematic and dynamic tasks.
- Emerging representations resemble place or direction cell coding.
- The model demonstrates integrated normalization within the neural architecture.

## Abstract

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.