Understanding the eye through data — from single synapses to population-scale disease.
01
Roa, C., Beuse, A., Schweig, A., ... Berens, P.
The German National Cohort: Ophthalmic Assessment, Baseline Profile, and Potential for Artificial Intelligence–Based Eye Research
02
Gonschorek, D., Oesterle, J., Zenkel, T., D’Agostino, F., Cai, C., Dyszkant, N., ... Berens P. & Euler, T.
A large-scale dataset of functional mouse ganglion cell layer responses
03
Lause J., Ziegenhain C., Hartmanis L., Berens P., Kobak D.
Compound models and Pearson residuals for single-cell RNA-seq data without UMIs.
04
05
Froudarakis, E., Cohen, U., Diamantaki, M., Patel, S., Tan, Z., Muhammad, T., ... & Tolias, A. S.
Object manifold geometry across the mouse cortical visual hierarchy
06
Froudarakis E., Cohen U., Diamantaki M., Patel S., Zheng T., Muhammad T., Walker E.Y., Reimer J., Berens P., Sompolinsky H., Tolias A.S.
Object manifold geometry across the mouse cortical visual hierarchy
07
González-Márquez, R., Berens, P., & Kobak
Cropping outperforms dropout as an augmentation strategy for self-supervised training of text embeddings
08
Ahlmann-Eltze, C., Barkmann, F., Lause, J., Boeva, V., & Kobak, D.
Representation learning of single-cell RNA-seq data
09
Ahlmann-Etze C., Barkmann F., Lause J., Boeva F. & Kobak D.