Abstract
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.
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 language | English |
|---|---|
| Title of host publication | From Physics to Control Through an Emergent View |
| Editors | Luigi Fortuna, Alexander Fradkov, Mattia Frasca |
| Publisher | World Scientific Publishing |
| Pages | 51-56 |
| Number of pages | 6 |
| Volume | 15 |
| ISBN (Electronic) | 9789814464710 |
| ISBN (Print) | 9789814313148 |
| DOIs | |
| Publication status | Published - 2010 |
Publication series
| Name | World Scientific Series on Nonlinear Science: Series B |
|---|---|
| Publisher | World Scientific Publishing |
Keywords
- NETWORK
- Neuron model
- Stochastic
- Delay
- Control