Speaker
Description
The main challenge in analyzing nonlinear dynamical systems lies in the repetitive and inefficient need to simulate each initial condition and parameter configuration separately. This approach not only increases computational cost but also limits scalability when exploring large parameter spaces. To address this issue, we developed a loop-based numerical methodology that automates the exploration of parameters and initial conditions within a unified framework. The method is applied to a brain-inspired nonlinear dynamical model with three parameters and multiple coupling strengths. This framework enables systematic categorization of system responses through statistical analysis and eigenvalue-based stability assessment, while accounting for multiple initial states. The results highlight clear distinctions between periodic, divergent, and non-divergent behaviors and demonstrate how variations in coupling strength, 𝑘𝑖𝑗, can induce transitions toward stable periodic dynamics across different conditions. By reducing redundancy and improving scalability, the proposed approach not only simplifies the analysis process but also provides a generalizable framework for studying broader classes of complex systems.
