In the Department of Data Science we generate knowledge from data to advance neuroscience and ophthalmology.
01
Strauss, S., Korympidou, M. M., Ran, Y., Franke, K., Schubert, T., Baden, T., Berens, P., Euler, T. & Vlasits, A. L
Center-surround interactions underlie bipolar cell motion sensitivity in the mouse retina
02
Boreiko, V., Ilanchezian, I., Ayhan, M. S., Müller, S., Koch, L. M., Faber, H., Berens, P. & Hein, M.
Visual explanations for the detection of diabetic retinopathy from retinal fundus images
03
Oesterle, J., Krämer, N., Hennig, P., & Berens, P.
Probabilistic solvers enable a straight-forward exploration of numerical uncertainty in neuroscience models
04
Faber, H., Berens, P., & Rohrbach, J. M.
Ocular changes as a diagnostic tool for malaria
05
Ayhan, M. S., Kümmerle, L. B., Kühlewein, L., Inhoffen, W., Aliyeva, G., Ziemssen, F., & Berens, P.
Clinical validation of saliency maps for understanding deep neural networks in ophthalmology
06
Behrens, C., Yadav, S. C., Korympidou, M. M., Zhang, Y., Haverkamp, S., Irsen, S., ... & Schubert, T.
Retinal horizontal cells use different synaptic sites for global feedforward and local feedback signaling.
07
Gonschorek, D., Höfling, L., Szatko, K. P., Franke, K., Schubert, T., Dunn, B., Berens, P. ... & Euler, T.
Removing inter-experimental variability from functional data in systems neuroscience.
08
BRAIN Initiative Cell Census Network (BICCN)
A multimodal cell census and atlas of the mammalian primary motor cortex
09
Yoshimatsu, T., Bartel, P., Schröder, C., Janiak, F. K., St-Pierre, F., Berens, P., & Baden, T.
Ancestral circuits for vertebrate color vision emerge at the first retinal synapse
10
Scala, F., Kobak, D., Bernabucci, M., Bernaerts, Y., Cadwell, C. R., Castro, J. R., [...], Berens, P. & Tolias, A. S.
Phenotypic variation of transcriptomic cell types in mouse motor cortex
11
Sokoloski, S., Aschner, A., & Coen-Cagli, R.
Modelling the neural code in large populations of correlated neurons
12
Ilanchezian, I., Kobak, D., Faber, H., Ziemssen, F., Berens, P., & Ayhan, M. S
Interpretable gender classification from retinal fundus images using BagNets.
13
Lause, J., Berens, P., & Kobak, D.
Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data.
14
Huang, Z., Ran, Y., Oesterle, Y., Euler, T., Berens, P.
Estimating smooth and sparse neural receptive fields with a flexible spline basis
15
Kobak, D., Bernaerts, Y., Weis, M. A., Scala, F., Tolias, A. S., & Berens, P.
Sparse reduced-rank regression for exploratory visualisation of paired multivariate data
16