Complex Structures and Behavior from Elementary Adaptive Network Automata

  • Daniel Wechsler
  • Ruedi StoopEmail author
Conference paper
Part of the Springer Proceedings in Physics book series (SPPHY, volume 191)


Adaptive networks are systems where a network structure evolves in interaction with, and depending on, node dynamics and where the node dynamics evolution depends on the actual network structure. This is a setting of fundamental relevance for neuronal culture development, for which often power law characteristics and indications of a critical state are found. Investigating an extremely simple instance of this computational paradigm of adaptive networks, we find particular rules and parameters for which power-law statistics emerge, which provides evidence that this fundamental framework is able to provide robust network structure evolution towards a critical state, an issue of great current interest.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute of Neuroinformatics and Institute for Computational ScienceUniversity of ZürichZurichSwitzerland
  2. 2.ETH ZürichZurichSwitzerland

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