Attractor Metadynamics in Adapting Neural Networks

  • Claudius Gros
  • Mathias Linkerhand
  • Valentin Walther
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8681)


Slow adaption processes, like synaptic and intrinsic plasticity, abound in the brain and shape the landscape for the neural dynamics occurring on substantially faster timescales. At any given time the network is characterized by a set of internal parameters, which are adapting continuously, albeit slowly. This set of parameters defines the number and the location of the respective adiabatic attractors. The slow evolution of network parameters hence induces an evolving attractor landscape, a process which we term attractor metadynamics. We study the nature of the metadynamics of the attractor landscape for several continuous-time autonomous model networks. We find both first- and second-order changes in the location of adiabatic attractors and argue that the study of the continuously evolving attractor landscape constitutes a powerful tool for understanding the overall development of the neural dynamics.


adiabatic attractors attractor metadynamics neural networks adaption homeostasis 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Claudius Gros
    • 1
  • Mathias Linkerhand
    • 1
  • Valentin Walther
    • 1
  1. 1.Institute for Theoretical PhysicsGoethe University FrankfurtGermany

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