Topic: mixture of linear regressions

Characterizing perceptual decision-making is an important goal of systems neuroscience. Recent work has shown that animal decision-making behavior is not stationary, but exhibits switches between discrete latent states within a single experimental session. However, fitting these complex models requires large amounts of data, a crucial impediment to progress. Here we propose to overcome this obstacle by introducing active learning methods for discrete latent variable models of decision-making. Active learning seeks to improve the efficiency of experiments by selecting highly informative stimul