Stochastic optimal control is a technique that allows the determination of a control signal to a noise-driven system to achieve a certain goal while minimizing certain costs. This talk will summarize recent efforts at controlling the firing times of a stochastic excitable system using stochastic optimal control. We consider both the cases where only a knowledge of the previous firing time is available, as well as the more information-rich case where one can monitor the ongoing voltage fluctuations. The talk will also consider the design of stimuli that can best reveal information about system parameters through a combination of information theory and stochastic optimal control. Finally we will present recent work on the control of sensory focus, in which the boundary between tonic and bursting activity serves as an optimal position where the Fisher information about an external input is maximized.