Controlling biological neural networks with deep reinforcement learning

Targeted interaction with networks in the brain is of immense therapeutic relevance. The highly dynamic nature of neuronal networks and changes with progressive diseases create an urgent need for closedloop control. Without adequate mathematical models of such complex networks, however, it remains unclear how tractable control problems can be formulated for neurobiological systems. Reinforcement learning (RL) could be a promising tool to address such challenges. Nevertheless, RL methods have rarely been applied to live, plastic neural networks. This study demonstrates that RL methods could help control response properties of biological neural networks with little prior knowledge of their complex dynamics.