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., 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
Froudarakis, E., Cohen, U., Diamantaki, M., Patel, S., Tan, Z., Muhammad, T., ... & 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-Etze C., Barkmann F., Lause J., Boeva F. & Kobak D.
Representation learning of single-cell RNA-seq data
09
Ahlmann-Eltze, C., Barkmann, F., Lause, J., Boeva, V., & Kobak, D.
Representation learning of single-cell RNA-seq data
10
Kadhim, K., Beck, J., Huang, Z., Macke, J. H., Rieke, F., Euler, T., ... & Berens, P.
A data and task-constrained mechanistic model of the mouse outer retina shows robustness to contrast variations
11
Schmors, L., Gonschorek, D., Böhm, J. N., Qiu, Y., Zhou, N., Kobak, D., ... & Berens, P.
TRACE: Contrastive learning for multi-trial time-series data in neuroscience.
12
Zouridis, I. S., Schmors, L., Lecca, S., Congiu, M., Mameli, M., Berens, P., ... & Burgalossi, A.
Aversion Encoding and Behavioral State Modulation of Physiologically Defined Cell Types in the Lateral Habenula.
13
Deistler, M., Kadhim, K. L., Pals, M., Beck, J., Huang, Z., Gloeckler, M., ... & Macke, J. H.
Jaxley: differentiable simulation enables large-scale training of detailed biophysical models of neural dynamics.
14
Müller, S., Koch, L. M., Lensch, H. P., & Berens, P.
Disentangling representations of retinal images with generative models
15
Schmidt, G., Heidrich, H., Berens, P., & Müller, S.
Learning Disease State from Noisy Ordinal Disease Progression Labels.
16
Ofosu Mensah, S., Djoumessi, K., & Berens, P.