We are data scientists with an interest in understanding the brain. Making sense of data is possibly the biggest problem in Neuroscience. We build algorithms to analyze data. We also use theory as well as computational and neural modeling to understand how information is processed in the nervous system, explaining data obtained in collaboration with electrophysiologists and in psychophysical experiments. Lastly, we constrain and develop new technologies aimed at obtaining data about brains.
Our conceptual work in the Bayesian Behavior Lab addresses information processing in the nervous system from two angles: (1) By analyzing and explaining electrophysiological data, we study what neurons do. (2) By analyzing and explaining human behavior, we study what all these neurons do together. Much of our work looks at these questions from a normative viewpoint, asking what problems the nervous system should be solving. This often means taking a Bayesian approach. Bayesian decision theory is the systematic way of calculating how the nervous system may make good decisions in the presence of uncertainty.