Controlling Dynamics of Complex Systems: Nonlinear Techniques vs Reinforcement Learning

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Complex nonlinear systems attract the attention of researchers from many different fields of contemporary science. One important phenomenon extensively studied theoretically and in experiments, is the emergence of a collective mode in oscillatory networks due to a synchronization transition. This collective mode can be either highly desirable or harmful. For example, it is hypothesized that excessive synchrony in neuronal networks is responsible for the emergence of pathological rhythms in epilepsies and in Parkinson’s disease. A major goal of the project is to develop techniques for synchronization control, combining feedback control with the modern sub-discipline of machine learning, the Reinforcement Learning. Having in mind a possible application to a medical technique, called Deep Brain Stimulation, we concentrate on control schemes based on the application of pulse action. Another area where nonlinearity is capable of affecting the problem both constructively and destructively is imaging. Controlling mode-mixing between noise and signal one can essentially enhance the image quality. Here, we plan to use the techniques for synchronization control to manipulate the spatial dynamics in an image-forming system.