Stability of entrainment of a continuum of coupled oscillators

Cooperation and Competition in Synchronous Open Quantum Systems

Synchronization between limit cycle oscillators can arise through entrainment to an external drive or through mutual coupling. The interplay between the two mechanisms has been studied in classical synchronizing systems, but not in quantum systems. Here, we point out that competition and cooperation between the two mechanisms can occur due to phase pulling and phase repulsion in quantum systems. We study their interplay in collectively driven degenerate quantum thermal machines and show that these mechanisms either cooperate or compete depending on the working mode of the machine (refrigerator or engine). The entrainment-mutual synchronization interplay persists with an increase in the number of degenerate levels, while in the thermodynamic limit of degeneracy, mutual synchronization dominates. Overall, our work investigates the effect of degeneracy and multilevel scaling of quantum synchronization and shows how different synchronizing mechanisms can cooperate and compete in quantum systems.

Control of neural systems at multiple scales using model-free, deep reinforcement learning

Recent improvements in hardware and data collection have lowered the barrier to practical neural control. Most of the current contributions to the field have focus on model-based control, however, models of neural systems are quite complex and difficult to design. To circumvent these issues, we adapt a model-free method from the reinforcement learning literature, Deep Deterministic Policy Gradients (DDPG). Model-free reinforcement learning presents an attractive framework because of the flexibility it offers, allowing the user to avoid modeling system dynamics. We make use of this feature by applying DDPG to models of low-level and high-level neural dynamics. We show that while model-free, DDPG is able to solve more difficult problems than can be solved by current methods. These problems include the induction of global synchrony by entrainment of weakly coupled oscillators and the control of trajectories through a latent phase space of an underactuated network of neurons. While this work has been performed on simulated systems, it suggests that advances in modern reinforcement learning may enable the solution of fundamental problems in neural control and movement towards more complex objectives in real systems.