Adaptive Bayesian Methods for Closed-Loop Neurophysiology

An important problem in the design of neurophysiology experiments is to select stimuli that rapidly probe a neuron's tuning or response properties. This is especially important in settings where the neural parameter space is multidimensional and the experiment is limited in time. Bayesian active learning methods provide a formal solution to this problem using a statistical model of the neural response and a utility function that quantifies what we want to learn. In contrast to staircase and other ad hoc stimulus selection methods, Bayesian active learning methods use the entire set of past stimuli and responses to make inferences about functional properties and select the next stimulus. Here we discuss recent advances in Bayesian active learning methods for closed-loop neurophysiology experiments. We review the general ingredients for Bayesian active learning and then discuss two specific applications in detail: (1) low-dimensional nonlinear response surfaces (also known as "tuning curves" or "firing rate maps") and (2) high-dimensional linear receptive fields. Recent work has shown that these methods can achieve higher accuracy in less time, allowing for experiments that are infeasible with nonadaptive methods. We conclude with a discussion of open problems and exciting directions for future research.