Multi-level Synergetic Computation in Brain
Mitja Peruš · 2001
Patterns of activities of neurons serve as attractors, since they are those neuronal configurations which correspond to minimal ’free energy’ of the whole system. Namely, they realize maximal possible agreement among constitutive neurons and are most-strongly correlated with some environmental pattern. Neuronal patterns-qua-attractors have both a material and a virtual aspect. As neuronal patterns, on the one hand, patterns-qua-attractors are explicit carriers of informational contents. As attractors, on the other hand, patterns-qua-attractors are implicit mental representations which acquire a meaning in contextual relations to other possible patterns. Recognition of an external pattern is explained as a (re)construction of the pattern which is the most relevant and similar to a given environmental pattern. The identity of the processes of pattern construction, re-construction and Hebbian short-term storage is realized in a net. Perceptual processes are here modeled using Kohonen’s topology-preserving feature mapping onto cortex where further associative processing is continued. To model stratification of associative processing because of influence from higher brain areas, Haken’s multi-level synergetic network is found to be appropriate. The hierarchy of brain processes is of ”software”-type, i.e. virtual, as well as it is of ”hardware”-type, i.e. physiological. It is shown that synergetic and attractor dynamics can characterize not only neural networks,