Statistical computations and neuronal mechanisms underlying complex decisions and behavior under uncertainty.  The work in the lab is theoretical in nature, and we collaborate with experimentalists in a close loop to refine both theories and experiments.

The goal of the Drugowitsch lab is to use tools from computational neuroscience to address how the nervous system represents and processes ambiguous information, and how this leads to efficient behavior.

Despite the noisy and ambiguous nature of the world that surrounds us, our nervous system deals with the resulting uncertainty in a remarkably efficient manner.  What are the computations that the brain performs to achieve such feats?  How does its biological substrate implement these computations?  How do we best interpret neural and behavioral data to guide our research?

To address these questions, we combine tools from machine learning, physics, and neuroscience to develop theories about how networks of neurons are able to infer, represent, and process the state of the world, and how this processing leads to competent behavior.  These theories are on one hand guided by statistical principles and the approximations required to keep the resulting computations tractable.  On the other hand, they are constrained by our knowledge of the architecture of the nervous systems, and by observed behavior and neural activity.

Current Lab Members:

2 Graduate Students

4 Postdoctoral Fellows

Selected Publications: