Delayed Feedback Control in Stochastic Excitable Networks

Natalia Janson, Andrey Pototsky, Sandhya Patidar

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review


A simplified model of a stochastic neural network is considered, being a system of a large number of identical excitable FitzHugh-Nagumo oscillators coupled via
the mean field. The possibility to control the global dynamics of this network is investigated. The control tool being probed is Pyrgas delayed feedback constructed
and applied through the mean field. It is shown that one can to destroy or diminish stochastic synchronization in a partially synchronized network by a weak delayed feedback under the appropriate choice of delay.
Original languageEnglish
Title of host publicationFrom Physics to Control Through an Emergent View
EditorsLuigi Fortuna, Alexander Fradkov, Mattia Frasca
PublisherWorld Scientific Publishing
Number of pages6
ISBN (Electronic)9789814464710
ISBN (Print)9789814313148
Publication statusPublished - 2010

Publication series

NameWorld Scientific Series on Nonlinear Science: Series B
PublisherWorld Scientific Publishing


  • Neuron model
  • Stochastic
  • Delay
  • Control


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