Probing Generic Integration Principles for Cognitive Functions

While physics gives us a picture how simple laws can drive the self-organization of matter at increasing complex scales of organization, biological evolution led to systems with processes enabling a rapid self-organization of information. Trying to replicate even remotely similar capabilities in artificial intelligent systems, such as robots, poses the challenge to find architectural principles that can integrate large collections of possibly heterogeneous functional elements into a coherently operating whole. We illustrate work on this issue by three concrete examples from our research: (i) an architecture for the self-organized formation of semantic maps by combining ideas from neural self-organization and non-euclidean spaces, (ii) a generic approach for image categorization by information compression motivated from analogies to compression-based structure formation in physics, and (iii) a layered competitive network architecture to decompose complex patterns into simpler constituents. Finally, we discuss how this research fits into the larger picture of developing and integrating elements towards artificial cognitive systems, in particular cognitive robots for human-machine interaction.