Dynamics of Complex Autonomous Boolean Networks

1. Front Matter

   Pages i-xx

   PDF

2. Introduction

   • David P. Rosin

   Pages 1-12

3. Previous Work on Boolean Networks

   • David P. Rosin

   Pages 13-23

4. Autonomous Boolean Networks on Electronic Chips

   • David P. Rosin

   Pages 25-33

5. Chaotic Dynamics of Autonomous Boolean Networks

   • David P. Rosin

   Pages 35-55

6. Ultra-Fast Physical Generation of Random Numbers Using Hybrid Boolean Networks

   • David P. Rosin

   Pages 57-79

7. Periodic Dynamics in Autonomous Boolean Networks

   • David P. Rosin

   Pages 81-105

8. Chimera Dynamics in Networks of Boolean Phase Oscillators

   • David P. Rosin

   Pages 107-132

9. Excitable Dynamics in Autonomous Boolean Networks

   • David P. Rosin

   Pages 133-151

10. Cluster Synchronization in Boolean Neural Networks

    • David P. Rosin

    Pages 153-169

11. Summary and Outlook

    • David P. Rosin

    Pages 171-172

12. Back Matter

    Pages 173-199

    PDF

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.
The author 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 presents the first experimental platform for large complex networks, which will facilitate exciting future developments.

• Autonomous Boolean Networks
• Chimera States
• Cluster Synchronization
• Complex Boolean Networks
• FPGA Book
• Field Programmable Gate Array
• Field Programmable Gate Array
• High-speed Random Number Generator
• Neural Spiking Satterns
• Neuro-inspired Computing
• complexity

• Institut für Theoretische Physik, Technische Universität Berlin, Berlin, Germany

  David P. Rosin

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.

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