In the paper “Automating the design of informative sequences of sensory stimuli” (by Lewi, Schneider, Woolley & Paninski, JCNS 11), the authors developed an algorithm to adaptively select stimuli during real-time sensory neurophysiology experiments. Given a set of already recorded responses, their algorithm determines which stimuli to present next so that the recorded data can provide as much information about the structure of the receptive field as possible.
Unlike their previous paper (NC 09), in this paper, they focused on the selection of informative stimulus “sequences” (or batches) to keep temporal or other types of correlations in stimuli. They denoted the length of sequence by b and talked about two cases, where b is some finite number and b goes to infinity. In both cases, selecting a sequence of stimuli turned out to be computationally challenging, so they developed lower bounds using Jensen’s inequality when computing the expected information gain. When b goes to infinity, they restricted the stimulus distribution to Gaussian to make the high dimensional optimization problem (over stimulus distribution) tractable. They tested the developed algorithm to real songbird auditory responses, and showed that the chosen stimulus sequences decreased the error significantly faster than i.i.d. experimental designs.
