My research focuses on two levels of computation. Models of high-level computation aim at understanding the representation that humans use to learn about their environment. The way information is represented constrains both the ways new information can be acquired and how learned information can be exploited for achieving various (e.g. behavioral) goals. Since learning has to be performed on high-dimensional, noisy and ambiguous stimuli, probabilistic models are adequate tools as these models can handle all of these issues. Furthermore, Bayesian probabilistic models provide a normative theory for learning, which enables us to compare model performance with human data. We test theories by designing psychophysics experiments in the visual domain.

 

My investigations in low-level computations seek for the way neurons deal with the problems imposed by the extremely rich stimuli. Learning in a neural architecture is determined by anatomical and physiological constraints. My focus is on how these constraints subserve necessary computations and how these constraints were adapted to the computations. I use probabilistic models to model evoked and spontaneous activities in the visual system.