Phone: +49 621 1703-2361
Fax: +49 621 1703-2005
Secretariat: Christine Roggenkamp, M.A.
Phone: +49 621 1703-6252, e-mail
Laboratory Building, 4th Floor, Room 4.18 / Room 4.02
In our department we follow two major lines of research:
1) We develop mathematical (neural network) models of brain function and, in particular, statistical approaches for inferring these models directly from experimental observations (e.g., multi-cell recordings or neuroimaging data). We are interested in both biophysically more detailed models of specific brain areas (e.g., Hass et al. 2016, PLoS Comp Biol) as well as in more abstract recurrent neural network models of neuronal dynamics (e.g., Durstewitz 2017, PLoS Comp Biol). Using statistical and machine learning approaches, parameters of these models are directly extracted (i.e., estimated in the statistical sense) from neuronal time series observations. These strongly data-driven models are then used to gain insight into the neuro-dynamical and neuro-computational processes underlying cognitive function, and in particular how these are altered in psychiatric conditions. On the applicational side, such models can be harvested to derive novel diagnostic and prognostic criteria for psychiatric conditions, and for devising more effective and individualized treatment options.
2) We develop and apply advanced statistical and machine learning algorithms for the analysis of high-dimensional time series data, such as simultaneous electrophysiological recordings from tens to hundreds of neurons, or functional magnetic resonance imaging (fMRI) data from human subjects. These are, for instance, methods that search for significant spatio-temporal patterns and activity constellations in experimentally recorded time series, while accounting for a number of possibly confounding factors like non-stationarity in the time series (e.g., Russo & Durstewitz 2017, eLife). Such reoccurring patterns where several neurons temporarily organize into specific spatio-temporal configurations (“cell assemblies”) are thought to play a huge role in the neural code, the “language of the brain”, but could also provide important and predictive signatures of psychiatric conditions that could be harvested as biomarkers for diagnostic or prognostic purposes.