Time, Anticipation, and Pattern Processors

Recent advances in the neurosciences are leading to an understanding of the structures and processes in neural networks as electric activation patterns, consisting of oscillation fields and logical relation structures of neuronal assemblies, treated formally as coupled dynamic systems and neuronal attractors. These are specifically characterized by their space-time-dynamics. In the present context, these phenomena are also called neuronal resonance patterns, and as higher-order hierarchical aggregates, patterns of patterns: metapatterns, as Gregory Bateson would have termed it. The term pattern is suited equally well for the spatial as for the temporal domain, and thus allows to formulate an abstract conceptual system of the neuronal computation processes of organisms. In reformulation of Goethe's original ideas, such a systematics of metapatterns is called meta-morphology, in an effort to account especially for their dynamic, time-relevant aspects. The fundamental properties of such a system display a strong resemblance to a very ancient thought system that was known as Pythagoreanism in the Western tradition. The present contribution will show some of the parallels between the ancient system and the meta-morphology as outlined here.