Kuramoto Model Metastability Analysis via Non-Reciprocal Adaptive Couplings

A 303.2 KB PDF presents numerical integration and bifurcation analysis of generalized Kuramoto models with non-reciprocal adaptive couplings. The study analyzes metastability as a function of system parameters like network connectivity and size, linking the deterministic dynamics to features of neuronal networks. Author Sayantan Nag Chowdhury published the work in March 2026.

Use Cases

• Analyze metastable switching in order parameters as a function of network connectivity and system size
• Study the role of individual oscillators as 'weak ties' between synchronized clusters in non-reciprocal adaptive couplings
• Model deterministic dynamics that induce random-featured time series in neuronal network simulations
• Investigate the effect of Hebbian versus anti-Hebbian coupling rules on cluster synchronization stability

Strengths

• Focus on a specific mechanism for inducing metastability in oscillatory systems, distinct from heteroclinic dynamics
• Analysis covers key parameters like network connectivity and system size
• Published under a permissive CC BY 4.0 license in March 2026

Limitations

• Data is contained within a single 303.2 KB PDF, limiting direct computational analysis
• No raw time series or numerical data tables are provided for independent model validation
• Scope is theoretical and simulation-based, not derived from empirical biological measurements

Provenance

Source

Sayantan Nag Chowdhury via figshare

Collection Method

Numerical integration and bifurcation analysis of generalized Kuramoto models

Time Range

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Freshness

Last updated March 2026.

Geography

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