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Controlling the Chimera Form in the Leaky Integrate-and-Fire Model
A Provata1, Ch G Antonopoulos2, P Vlamos3
1Institute of Nanoscience and Nanotechnology, National Center for Scientific Research "Demokritos", Athens, Greece. a.provata@inn.demokritos.gr.
Advances in Experimental Medicine and Biology
|January 1, 2022
Summary
Broken connectivity in neuronal networks causes coherent chimera states to localize around anomalies. Altering oscillator frequencies shifts incoherent domains toward the anomaly, aiding in detecting brain network disruptions.
Area of Science:
- Computational Neuroscience
- Network Science
- Nonlinear Dynamics
Background:
- Coupled neuronal oscillators exhibit complex synchronization patterns, including chimera states (coexisting coherent and incoherent domains).
- Real-world neural networks often feature imperfect connectivity due to biological factors or damage.
- Understanding how network structure affects dynamics is crucial for diagnosing brain disorders.
Purpose of the Study:
- To investigate the impact of broken connectivity and frequency disorder on chimera states in coupled neuronal oscillators.
- To explore the potential of these phenomena for detecting anomalies in brain networks.
Main Methods:
- Simulations of nonlinear oscillator systems (e.g., Kuramoto, FitzHugh-Nagumo) with nonlocal coupling.
- Analysis of network synchronization using the Kuramoto synchronization index.
- Modeling scenarios with both disrupted connectivity and altered oscillator frequencies.
Main Results:
- Destructed connectivity causes coherent chimera domains to symmetrically localize around the anomaly.
- Network synchronization decreases as the size of the destructed region increases.
- Altering oscillator frequencies, with intact connectivity, shifts incoherent chimera domains towards the anomaly.
Conclusions:
- Network anomalies can be identified by observing the behavior of chimera states under connectivity disruptions or frequency alterations.
- This approach shows promise for detecting small, subtle anomalies in brain network structures.
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