Cortical chunks learning for action selection in a complex task

For low level behaviors, navigational trajectories can be encoded as attraction basin resulting from associations between visual based localization and directions to follow. The use of other sensory information such as contexts for modifying the behavior needs a specialized learning. In this paper, we propose a minimal model using multimodal contexts, and a mechanism for obtaining a better generalization of the contexts and creation of chunks. We briefly present the bases of the sensory-motor architecture, and explain the neurobiological principal inspiring this model. We also evaluate the proposed improvement on simulated signals and in a robotic navigational experiment.