Dynamics of Complex Autonomous Boolean Networks

ISBN-13: 9783319135779 / Angielski / Twarda / 2015 / 199 str.

David Rosin

Opis

This thesis focuses on the dynamics of autonomous Boolean networks, on the basis of Boolean logic functions in continuous time without external clocking. These networks are realized with integrated circuits on an electronic chip as a field programmable gate array (FPGA) with roughly 100,000 logic gates, offering an extremely flexible model system. It allows fast and cheap design cycles and large networks with arbitrary topologies and coupling delays.
Theauthor presents pioneering results on theoretical modeling, experimental realization, and selected applications.In this regard, three classes of novel dynamic behavior are investigated: (i) Chaotic Boolean networks are proposed as high-speed physical random number generators with high bit rates. (ii) Networks of periodic Boolean oscillators are home to long-living transient chimera states, i.e., novel patterns of coexisting domains of spatially coherent (synchronized) and incoherent (desynchronized) dynamics. (iii) Excitable networks exhibit cluster synchronization and can be used as fast artificial Boolean neurons whose spiking patterns can be controlled. This work presentsthe firstexperimental platform for large complex networks, which will facilitate excitingfuture developments."

Spis treści

Introduction.- Previous Work on Boolean Networks.- Autonomous Boolean Networks on Electronic Chips.- Chaotic Dynamics of Autonomous Boolean Networks.- Ultra-Fast Physical Generation of Random Numbers Using Hybrid Boolean Networks.- Periodic Dynamics in Autonomous Boolean Networks.- Chimera Dynamics in Networks of Boolean Phase Oscillators.- Excitable Dynamics in Autonomous Boolean Networks.- Cluster Synchronization in Boolean Neural Networks.- Summary and Outlook.

O autorze

David Rosin studied physics at Technische Universität Berlin, Germany, and received his Bachelor Degree in 2009 and his Master Degree in 2011. He researched complex dynamical networks in the framework of a collaboration between Daniel J. Gauthier from Duke University (USA) and Eckehard Schöll from Technische Universität Berlin (Germany) from 2010 till 2014. This work was submitted as a Doctoral Thesis in physics at Technische Universität Berlin in 2014.