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Synchronization of multi-frequency noise-induced oscillations
Sergey Astakhov1, Alexey Feoktistov, Vadim S Anishchenko
1Saratov State University, 410012 Saratov, Russia. s.v.astakhov@gmail.com
Chaos (Woodbury, N.Y.)
|January 10, 2012
Summary
This study reveals that FitzHugh-Nagumo models exhibit similar synchronization patterns for both self-sustained and noise-induced oscillations. These findings are crucial for understanding neural dynamics and synchronization in complex systems.
Area of Science:
- Computational Neuroscience
- Nonlinear Dynamics
- Complex Systems
Background:
- The FitzHugh-Nagumo model is a fundamental neural model describing neuron excitability.
- Understanding synchronization is key to comprehending neural network function.
- Investigating noise-induced phenomena in excitable systems is an active research area.
Purpose of the Study:
- To compare synchronization behaviors in self-sustained and noise-induced oscillations within an excitable system.
- To analyze frequency locking phenomena in the FitzHugh-Nagumo model with multiple spectral frequencies.
- To explore the role of noise in generating stable and unstable limit cycles and tori.
Main Methods:
- Utilized a FitzHugh-Nagumo model in its excitable regime.
- Analyzed systems with more than one dominant frequency in their spectral analysis.
- Investigated bifurcations, including tangential bifurcations, of noise-induced attractors.
Main Results:
- Demonstrated that the excitable FitzHugh-Nagumo system exhibits identical frequency lockings to self-sustained quasiperiodic oscillators.
- Identified the presence of noise-induced stable and unstable limit cycles and tori.
- Characterized tangential bifurcations associated with these noise-induced phenomena.
Conclusions:
- The synchronization dynamics of self-sustained and noise-induced oscillations are similar in this excitable neural model.
- Noise plays a significant role in shaping the oscillatory and synchronization properties of neural models.
- These findings have substantial implications for neuroscience, particularly in understanding neural information processing and network behavior.
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