Understanding the eye through data — from single synapses to population-scale disease.
01
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
02
Lause J., Ziegenhain C., Hartmanis L., Berens P., Kobak D.
Compound models and Pearson residuals for single-cell RNA-seq data without UMIs.
03
04
Froudarakis, E., Cohen, U., Diamantaki, M., Patel, S., Tan, Z., Muhammad, T., ... & Tolias, A. S.
Object manifold geometry across the mouse cortical visual hierarchy
05
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
06
González-Márquez, R., Berens, P., & Kobak
Cropping outperforms dropout as an augmentation strategy for self-supervised training of text embeddings
07
Ahlmann-Eltze, C., Barkmann, F., Lause, J., Boeva, V., & Kobak, D.
Representation learning of single-cell RNA-seq data
08
Ahlmann-Etze C., Barkmann F., Lause J., Boeva F. & Kobak D.