I am heading the Computational Systems Neuroscience Lab at the Department of Computational Sciences, MTA Wigner Research Center for Physics. I earned my PhD from Eötvös University under the supervision of Péter Érdi. My postdoctoral work was with Eörs Szathmáry in Collegium Budapest, Institute for Advanced Study and later joined József Fiser’s lab at the Volen Centre at the Brandeis University as a Swartz Postdoctoral Fellow. Later, I was awarded a Marie Curie Posdoctoral Fellowship and joined to Daniel Wolpert and Máté Lengyel at the Computational and Biological Learning Lab at the University of Cambridge.
Our 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 analyzing behavior of humans in experiments: by following participants’ eye movement we analyze how learning affects the design of efficient movement strategies.
Our investigations in low-level computations address how neurons deal with the problems imposed by the extremely rich stimuli. Optimal inference and learning requires that neurons also represent the uncertainty related to the inferred features of the environment besides the actual values of the features. The focus is on how a proper representation can be built and how these principles affect neural responses. Probabilistic models are used to model evoked and spontaneous activities in the visual system.