Spiral wave elimination in memristive neuronal networks under dynamic learning modulated magnetic stimulation

Abnormal spiral wave discharges frequently occur in neocortical neural networks, and there is an urgent need for low-intensity, precise intervention to suppress spiking instability. Existing regulatory approaches struggle to keep pace with the dynamic evolution of neural electrical activity, and there is a lack of effective adaptive control methods. This paper puts forward an adaptive magnetic stimulation regulation strategy based on dynamic learning of synchronization (DLS). It can effectively suppress spiral waves via dynamic adjustment of electromagnetic induction strength in memristor Hodgkin-Huxley neuronal networks. We compared three control modes: uniform magnetic stimulation (UMS), alternating magnetic stimulation (AMS), and dynamic learning modulated magnetic stimulation (DLMMS). The results indicate that: the DLMMS can achieve global synchronization of the network at electromagnetic induction strength far lower than that required by the other two modes. Furthermore, its control performance is closely correlated with spatial resolution and learning rate. This finding may provide a new theoretical basis and implementation pathway for the potential application of intelligent optimization algorithms in the development of low-intensity, highly efficient methods for eliminating spiral waves.