Complex Neural Architectures for Emerging Cognitive Abilities in an Autonomous System

In this paper, we propose a novel neural architecture named PerAc which is a systemetic way to decompose the control of an autonomous robot in perception and action flows. We first present an application of the PerAc architecture to the simulation of a vision system with a moving eye. Then we propose a second application where the robot learns to return from any starting place to a previously discovered and learned position without any a priori symbolic representation.