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Imperfect traveling chimera states induced by local synaptic gradient coupling
Bidesh K Bera1, Dibakar Ghosh1, Tanmoy Banerjee2
1Physics and Applied Mathematics Unit, Indian Statistical Institute, Kolkata-700 108, India.
Physical Review. E
|August 31, 2016
Summary
Researchers discovered novel chimera patterns in neuronal networks, including an imperfect traveling chimera state. These complex brain dynamics emerge even with simplified one-way local coupling, broadening conditions for chimera state formation.
Area of Science:
- Neuroscience
- Complex Systems
- Network Science
Background:
- Chimera states, a mix of synchronized and desynchronized behavior, have been observed in various oscillatory networks.
- Previous research suggested specific coupling types (nonlocal, global, or nearest-neighbor local) were necessary for chimera states.
Purpose of the Study:
- To investigate chimera patterns in neuronal oscillator networks with local, synaptic gradient coupling.
- To identify novel chimera states and understand the conditions for their emergence.
- To explore the impact of coupling types on network dynamics.
Main Methods:
- Utilized a network of identical bursting Hindmarsh-Rose neuronal oscillators.
- Implemented local, synaptic gradient coupling between oscillators.
- Analyzed spatiotemporal behaviors across parameter space to map emergent patterns.
Main Results:
- Observed chimera patterns, including a new 'imperfect traveling chimera state' where incoherence invades coherence.
- Demonstrated chimera states can arise with one-way local coupling, challenging prior assumptions.
- Identified transitions between chimera patterns, in-phase synchronization, and global amplitude death states based on coupling strengths.
Conclusions:
- Local, synaptic gradient coupling is sufficient for generating complex chimera states in neuronal networks.
- The emergence of chimera states is possible under more relaxed connectivity constraints than previously thought.
- The study provides a comprehensive map of network behaviors, aiding in understanding complex neuronal dynamics.
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