Adaptation and Contraction Theory for the Synchronization of Complex Neural Networks

  • Pietro DeLellis
  • Mario di Bernardo
  • Giovanni Russo
Part of the Springer Series in Cognitive and Neural Systems book series (SSCNS, volume 3)


In this chapter, we will present two different approaches to solve the problem of synchronizing networks of interacting dynamical systems. The former will be based on making the coupling between agents in the network adaptive and evolving so that synchronization can emerge asymptotically. The latter will be using recent results from contraction theory to give conditions on the node dynamics and the network topology that result into the desired synchronized motion. The theoretical results will be illustrated by means of some representative examples, including networks of neural oscillators.


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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Pietro DeLellis
    • 1
  • Mario di Bernardo
    • 1
    • 2
  • Giovanni Russo
    • 1
  1. 1.Department of Systems and Computer EngineeringUniversity of Naples Federico IINaplesItaly
  2. 2.Department of Engineering MathematicsUniversity of BristolBristolUK

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