Elimination of spiral waves in a locally connected chaotic neural network by a dynamic phase space constraint

Turbulence control in memristive neural network via adaptive magnetic flux based on DLS-ADMM technique

High-voltage defibrillation for eliminating cardiac spiral waves has significant side effects, necessitating the pursuit of low-energy alternatives for a long time. Adaptive optimization techniques and machine learning methods provide promising solutions for adaptive control of cardiac wave propagation. In this paper, the control of spiral waves and turbulence, as well as 2D and 3D heterogeneity in memristive neural network by using adaptive magnetic flux (AMF) is investigated based on dynamic learning of synchronization - alternating direction method of multipliers (DLS-ADMM). The results show that AMF can achieve global electrical synchronization under multiple complex conditions. There is a trade-off between AMF accuracy and computational speed, lowering the resolution of AMF requires a higher flux of magnetic fields to achieve the network synchronization, resulting in an increase in average Hamiltonian energy, which implies greater energy consumption. The AMF method is more energy efficient than existing DC and AC methods, but it relies on adequate resolution. The ADMM constraints can enhance the synchronization robustness and energy efficiency of DLS techniques, albeit at the cost of increased the computational complexity. The adaptive elimination of spiral waves and turbulence using AMF presented in this paper may provide a novel approach for the low-energy defibrillation studies, and its practical application and performance enhancement deserve further research.