Self-organizingly emerging activeness architecture realized by coherent neural networks

Coherent activeness architecture: a self-organizing activeness (or intentionality) architecture using coherent neural networks is proposed for the future brain-type information processing systems. It utilizes coherent neural network modules whose behavior (forward processing, learning, self-organization, etc.) is controlled by their carrier-wave frequencies. The behaviors constructs an orthogonal basis-function set in frequency domain. Therefore the network is able to obtain an arbitrary (continuous/discrete) property as a combinatorial profile. Through this mechanism, an intentionality-controlling network self-organizes adaptively into a symbol-, pattern-, or intermediate processing circuit. The architecture realizes active behaviors such as attentions in recognition tasks and mode selections in motor actions that are considered indispensable for the future brain-type systems. Simulation results are also presented which demonstrate an environment-dependent self-organization of the activeness mechanism