Corticonic Networks for Higher-Level Processing

N.H. Farhat (USA)


Cortex, Logistic-Map, Parametric Coupling, Mutual Information, Dynamic-Memory, Self Organization


A set of equations seeking to model the way the cortex interacts with subcortical areas to produce certain higher-level brain functions is described. The equations are those of a network of parametrically coupled maps that incorporates salient properties of the cortex. Justifications for this approach and demonstration of its effectiveness for a parametrically coupled logistic map network (PCLMN) are presented. The PCLMN can self organize under information driven adaptation, is capable of handling dynamic (spatio-temporal) input patterns, furnishes an enormous number of attractors for inputs to choose from, plus other intriguing features that can be used in the design of intelligent systems.

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