Foundation of re-normalized synergetics - issues of computability and complexity 01/2010;


We consider issues of computability and complexity in statistical physics from the perspective of information theory. It assumes information coupling by a mass conservation. Finally, we explain here our view on the 'mass phenomenon' in the clusters of information.

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    ABSTRACT: SCIENTIFIC AND TECHNOLOGICAL OBJECTIVES Stochastic Resonance Synergetics is a generalized description of an intelligent agent, with a memory and means of action, and its interactions with the environment. It studies coding, control, and propagation of information. This paradigm of computation recognizes an internal state of networked neural system and its interaction with the environment via coupled information propagation. It combines spatial and a dimension of scale in a dynamical system of computation, in the scale-space. The SRS hypothesis aims for building a study discipline of intelligent agents that uses a quantitative analysis of attention, memory and behavioral data. It requires a cross psucho-physics, bio-imaging and computer science, machine learning approaches to achieve:  A proof of the concept in multi-dimensional, information-driven situations: unpredictable and/or evolutions in a systemic or environmental nature; both, a system of intelligent agents and environments may entrain each other at different spatio-temporal and dynamic scales.  Diagnosis of disordered states and self-adjustment to those aligned with the environment.  Coordination of motion of the self-adjusted intelligent agent systems for different (structural as well as functional) morphodynamic and evolutionary purposes. The main goal of the project is to further develop and validate scale-space approaches using renormalization techniques that yield hierarchies of robust regimes of synergetics
    Full-text · Chapter · Jul 2012