Abstract
Adaptive stimulus optimization refers to an experimental approach in neuroscience where neuronal or behavioral responses to stimuli presented on previous trials are utilized to adaptively generate new stimuli in an iterative, closed-loop manner, usually by optimizing an objective function. There are different choices for the objective function. For example, if the objective function is the neural response itself, the optimization procedure finds an optimal stimulus that drives maximum response or is at least a local optimum in the stimulus space. When the objective function is the mutual information between the responses and the unknown parameters of a stimulus-response model, the optimization finds the stimulus set that yields the most accurate parameter estimation.