Expertise
Our research is at the interface of computer science and the brain sciences. We are interested in understanding how neural systems make sense of information in the environment, resulting in complex inferences, perception, and behavior.
We are currently pushing the notion of learning statistical regularities in visual scenes to make testable predictions about how cortical circuits process natural scenes. We are also focusing on understanding how the brain processes visual information hierarchically and the role of canonical nonlinearities.
Using vision as a paradigmatic example, we are currently particularly interested in understanding how the brain processes visual information hierarchically, to build up more complex representations. We are combining recent advances in deep learning, with approaches we have developed for modeling nonlinearities in Primary Visual Cortex, to make headway in understanding Secondary and higher areas of visual cortex.