Fundamentals of Information Processing on an Analog Reservoir Computer

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Physical dynamical systems are able to process information in a nontrivial manner.  In this thesis we evaluate the information processing capacity (IPC) in an experimental setup to assess information processing in a reservoir computer that consists of an analog Mackey-Glass nonlinearity coupled to itself via a delay line. We link the different dynamical regimes of this system to distinct modes of information processing and assess the influence of various dynamical phenomena, such as fixed point, periodic and chaotic dynamics on computation carried out by the system. We measure nonlinear memory up to seventh order and give its distribution as a function of the system parameters. Further we explore the influence of noise by performing matching numerical simulations. Thereby we find that the presence of noise, which is inevitable in every experimental setup, does not homogeneously degrade a system's computational power as measured by the IPC, but instead implies a change in the distribution of capacity across the degrees of information processing observed in the system. Finally, we use theoretical considerations from the literature on the IPC to explain our observations and distill suggestions to guide experimental setup.



Supervisors: André Röhm and Miguel C. Soriano



Jury: Ingo Fischer, Roberta Zambrini, Miguel Cornelles Soriano



Zoom link: https://us02web.zoom.us/j/85660934743?pwd=MU5xckQ5aVhKbkZSV2VzU2lUVEowQT09



Contact details:

Miguel C. Soriano
971 17 13 14
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