The mathematical optimization techniques may control the network to target firing patterns by adjusting the weights of network nodes. Inspired by the dynamics of dynamical learning, we recently proposed a technique for dynamic learning of synchronous (DLS) to control the firing state of neural networks. In this study, we apply the DLS technique to a Hodgkin-Huxley-style neural network, and investigate in regular, random, small-world and scale-free networks. We use the DLS technique to accomplish the network adaptive global synchronization, adaptive local synchronization, and phase locking with...
