Data Collection for "Reinforcement learning for closed-loop optimisation of spatiotemporal stimulation in patterned neuronal networks"

In the paper, we embedded topologically constrained biological neuronal networks in a closed-loop reinforcement learning (RL) framework that sends electrical stimuli to the MEA and evaluates the evoked responses to efficiently identify stimulation patterns that evoke specific target activity motifs.

We first demonstrated that stimulation-evoked responses in engineered recurrent networks were stable and separable across the action space over hours of continuous operation. We characterised the dependency of responses on prior stimulation history, finding state dependence in a subset of stimulus pairs. We then benchmarked different RL agents, multi-armed bandits (MABs) and linear contextual bandits (LCBs), on the task of identifying stimulation patterns that maximise the length of clockwise-circular firing sequences.