Computational Neuroscience

The joint research initiative between the Haubensak Laboratory at the IMP and the Bühler Group at VRVis aims at identifying neuronal circuitry underlying brain functions related to ethologically and/or biomedically relevant behavioral traits. IMP and VRVis will jointly realize computational neuroscience approaches to the above topic including methods to fuse, mine and analyse big brain data in context of data stemming from own experiments. This will be in particular

1. Computational neuroanatomy. We seek to fuse heterogenous genetic and brain data for insight into the functional genetic and functional neuroanatomy of behavioral traits (Ganglberger et al., 2018). The specific work package involves mapping evolutionary genetic data on brain networks using established workflows. These data will be related to the neurocognitive profiles of the respective species.

2. Decoding of neuronal population data. We will use statistical and machine learning approaches to decode how affective stimuli and behavioral states are processed in neuronal circuit dynamics (Grössl et al., 2018; Pliota et al., 2018). The neuronal activity data from deep brain Ca imaging and in vivo recordings and BOLD fMRI experiments. This neuronal activity data originates from various commercial and custom software pipelines will be related to stimulus presentation and behavioral states of the animals.

3. Computational ethology. We are using data driven approaches for automated classification of behavioral states and patterns. The behavioral data originate from various commercial and custom software. Different data mining approaches will have to be applied and verified.

4. Psychophysical modelling. We fit our neuronal activity and behavioral data to basic psychophysical models to identify critical parameters in controlling behavioral responding. Literature inspired psychophysical models will be adapted to our specific behavioral learning and decision making tasks and data (Task 3). Critical parameters will then be mapped to underlying neuronal architecture and dynamics (Task 2).


F. Ganglberger, J. Kaczanowska, J. M. Penninger, A. Hess, K. Bühler, and W. Haubensak, “Predicting functional neuroanatomical maps from fusing brain networks with genetic information,” NeuroImage, vol. 170, pp. 113–120, Apr. 2018.

Groessl, F., Munsch, T., Meis, S., Griessner, J., Kaczanowska, J., Pliota, P., Kargl, D., Badurek, S., Kraitsy, K., Rassoulpour, A., Zuber, J., Lessmann, V., Haubensak, W. (2018) Dorsal tegmental dopamine neurons gate associative learning of fear.Nat Neurosci. 21(7):952-962

Pliota, P., Böhm, V., Grössl, F., Griessner, J., Valenti, O., Kraitsy, K., Kaczanowska, J., Pasieka, M., Lendl, T., Deussing, JM., Haubensak, W. (2018) Stress peptides sensitize fear circuitry to promote passive coping.Mol Psychiatry.