Neurobiology suggests the design of modular architectures for neural control

Abstract

The existence of modular structures in the biological world strongly suggests that the training of this kind of structures is actually feasible. It is a key indication for the development of neural networks applications especially in the field of robotics. Indeed, a single network can only efficiently treat problems with few independent variables; the combining of several networks is necessary to address more complex tasks. We investigate learning techniques and show that using a particular form of architecture can ease the training of a modular structure: a bi-directional structure that allows combining several neural networks. The approach is illustrated with Kohonen's self-organizing maps for a robotic visual sensing task.