Dynamic Entrainment: A deep learning and data-driven process approach for synchronization in the Hodgkin-Huxley model

Resonance and synchronized rhythm are important phenomena and can be either constructive or destructive in dynamical systems in the nature, specifically in biology. There are many examples showing that the human’s body organs must maintain their rhythm in order to function properly. For instance, in the brain, synchronized or desynchronized electrical activities can lead to neurodegenerative disorders such as Huntington’s disease. In this paper, we adopt a well known conductance based neuronal model known as Hodgkin-Huxley model describing the propagation of action potentials in neurons. Armed with the “data-driven” process alongside the outputs of the Hodgkin-Huxley model, we introduce a novel Dynamic Entrainment technique, which is able to maintain the system to be in its entrainment regime dynamically by applying deep learning approaches.