<![CDATA[The human brain is a highly complex system whose dynamics cannot be fully understood through a single disciplinary perspective. This study aims to examine the synchronization of neural networks by combining theoretical physics, neuroscience, and computational methods. The research employed two main approaches: computer simulations based on the Kuramoto oscillator model and empirical analysis of electroencephalography (EEG) data. The simulation modeled neural activity using a graph-theoretical framework, while EEG analysis provided time-series data of brainwave patterns. Both results were compared to validate the accuracy of the model. Findings show that synchronization levels from simulations closely resemble EEG data, with only minor differences across various frequency conditions. Notably, both results revealed a tendency for stronger synchronization at higher frequencies, indicating a collective mechanism of neural coordination. These results demonstrate that physics-based models can effectively represent biological phenomena, while empirical data ensures that the findings remain grounded in real neural dynamics. The integration of theoretical and empirical approaches highlights the importance of interdisciplinary collaboration in studying brain complexity. This research not only contributes to a deeper scientific understanding but also opens potential applications in neuroscience, clinical diagnostics, and computational modeling. Overall, the study reinforces that interdisciplinary frameworks are essential for bridging abstract theories with biological realities.]]>
