TITLE:
Stochastic Resonance Synergetics—Quantum Information Theory for Multidimensional Scaling
AUTHORS:
Milan Jovovic
KEYWORDS:
Quantum Information Theory, Stochastic Resonance, Dynamical Maps, Neuroscience, Algorithm Design
JOURNAL NAME:
Journal of Quantum Information Science,
Vol.5 No.2,
June
5,
2015
ABSTRACT: A quantum
information theory is derived for multidimensional signals scaling. Dynamical data
modeling methodology is described for decomposing a signal in a coupled structure
of binding synergies, in scale-space. Mass conservation principle, along with a
generalized uncertainty relation, and the scale-space wave propagation lead to a
polynomial decomposition of information. Statistical map of data, through dynamical
cascades, gives an effective way of coding and assessing its control structure.
Using a multi-scale approach, the scale-space wave information propagation is utilized
in computing stochastic resonance synergies (SRS), and a data ensemble is conceptualized
within an atomic structure. In this paper, we show the analysis of multidimensional
data scatter, exhibiting a point scaling property. We discuss applications in image
processing, as well as, in neuroimaging. Functional neuro-cortical mapping by multidimensional
scaling is explained for two behaviorally correlated auditory experiments, whose
BOLD signals are recorded by fMRI. The point scaling property of the information
flow between the signals recorded in those two experiments is analyzed in conjunction
with the cortical feature detector findings and the auditory tonotopic map. The
brain wave nucleons from an EEG scan, along with a distance measure of synchronicity
of the brain wave patterns, are also explained.