Dynamic tracking and adaptive optimal training of decision-making behavior

While animal training is an essential part of modern psychology experiments, the training protocol is usually hand-designed, relying heavily on trainer intuition and guesswork. I will present a general framework that takes animal training to a quantitative problem, building on ideas from reinforcement learning and optimal experimental design. Our work addresses two interesting problems at once: First, we develop an efficient method to characterize an animal's behavioral dynamics during learning, and infer the learning rules underlying its behavioral changes. Second, we formulate a theory for optimal training, which involves selecting sequences of stimuli that will drive the animal’s internal policy toward a desired state in the parameter space according to the inferred learning rules.