In the Department of Data Science we generate knowledge from data to advance neuroscience and ophthalmology.
01
Ilanchezian, I., Kobak, D., Faber, H., Ziemssen, F., Berens, P., & Ayhan, M. S
Interpretable gender classification from retinal fundus images using BagNets.
02
Lause, J., Berens, P., & Kobak, D.
Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data.
03
Huang, Z., Ran, Y., Oesterle, Y., Euler, T., Berens, P.
Estimating smooth and sparse neural receptive fields with a flexible spline basis
04
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
05
06
Schroeder, C., Oesterle, J., Berens, P., Yoshimatsu, T., & Baden, T.
Distinct synaptic transfer functions in same-type photoreceptors.
07
Karlinsky, A., & Kobak, D.
Tracking excess mortality across countries during the COVID-19 pandemic with the World Mortality Dataset.
08
Kobak, D., & Linderman, G. C.
Initialization is critical for preserving global data structure in both t-SNE and UMAP
09
Baden, T., Euler, T., & Berens, P.
Understanding the retinal basis of vision across species.
10
Schröder, C., Klindt, D., Strauss, S., Franke, K., Bethge, M., Euler, T., & Berens, P.
System identification with biophysical constraints: A circuit model of the inner retina.
11
Oesterle, J., Behrens, C., Schröder, C., Hermann, T., Euler, T., Franke, K., ... & Berens, P.
Bayesian inference for biophysical neuron models enables stimulus optimization for retinal neuroprosthetics
12
Pfau, M., Walther, G., von der Emde, L., Berens, P., Faes, L., Fleckenstein, M., ... & Holz, F. G.
Artificial intelligence in ophthalmology: Guidelines for physicians for the critical evaluation of studies.
13
Ayhan, M. S., Kühlewein, L., Aliyeva, G., Inhoffen, W., Ziemssen, F., & Berens, P.
Expert-validated estimation of diagnostic uncertainty for deep neural networks in diabetic retinopathy detection.
14
Laturnus, S., von Daranyi, A., Huang, Z., & Berens, P.
MorphoPy: A python package for feature extraction of neural morphologies.
15
Yoshimatsu, T., Schröder, C., Nevala, N. E., Berens, P., & Baden, T.
Fovea-like photoreceptor specializations underlie single UV cone driven prey-capture behavior in zebrafish.
16
Szatko, K. P., Korympidou, M. M., Ran, Y., Berens, P., Dalkara, D., Schubert, T., ... & Franke, K.