In the Department of Data Science we generate knowledge from data to advance neuroscience and ophthalmology.
01
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
02
Behrens, C., Yadav, S. C., Korympidou, M. M., Zhang, Y., Haverkamp, S., Berens, P. & Schubert, T.
Retinal horizontal cells use different synaptic sites for global feedforward and local feedback signaling.
03
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.
04
BRAIN Initiative Cell Census Network (BICCN)
A multimodal cell census and atlas of the mammalian primary motor cortex
05
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
06
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
07
Sokoloski, S., Aschner, A., & Coen-Cagli, R.
Modelling the neural code in large populations of correlated neurons
08
Ilanchezian, I., Kobak, D., Faber, H., Ziemssen, F., Berens, P., & Ayhan, M. S
Interpretable gender classification from retinal fundus images using BagNets.
09
Lause, J., Berens, P., & Kobak, D.
Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data.
10
Huang, Z., Ran, Y., Oesterle, Y., Euler, T., Berens, P.
Estimating smooth and sparse neural receptive fields with a flexible spline basis
11
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
12
13
Schroeder, C., Oesterle, J., Berens, P., Yoshimatsu, T., & Baden, T.
Distinct synaptic transfer functions in same-type photoreceptors.
14
Karlinsky, A., & Kobak, D.
Tracking excess mortality across countries during the COVID-19 pandemic with the World Mortality Dataset.
15
Kobak, D., & Linderman, G. C.
Initialization is critical for preserving global data structure in both t-SNE and UMAP
16
Baden, T., Euler, T., & Berens, P.