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Nominated as an outstanding Ph.D. thesis by the University of Lancaster, UK
Describes a new inference technique for time-evolving coupled systems in the presence of noise
Includes the first reconstruction of a time-evolving coupling function between open (biological) systems
This thesis presents a new method for following evolving interactions between coupled oscillatory systems of the kind that abound in nature. Examples range from the subcellular level, to ecosystems, through climate dynamics, to the movements of planets and stars. Such systems mutually interact, adjusting their internal clocks, and may correspondingly move between synchronized and non-synchronized states. The thesis describes a way of using Bayesian inference to exploit the presence of random fluctuations, thus analyzing these processes in unprecedented detail. It first develops the basic theory of interacting oscillators whose frequencies are non-constant, and then applies it to the human heart and lungs as an example. Their coupling function can be used to follow with great precision the transitions into and out of synchronization. The method described has the potential to illuminate the ageing process as well as to improve diagnostics in cardiology, anesthesiology and neuroscience, and yields insights into a wide diversity of natural processes.
Content Level »Research
Keywords »Bayesian Inference and Random Fluctuations - Biomedical Examples of Coupled Oscillators - Climate Dynamics as Coupled Oscillators - Coupled Non-synchronous States - Coupled Oscillators - Coupled Oscillatory Systems in Nature - Coupling Function for Interacting Oscillators - Heart-Lung Synchronization - Interacting Oscillators - Non-autonomous Systems