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., 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
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
González-Márquez, R., Berens, P., & Kobak
Cropping outperforms dropout as an augmentation strategy for self-supervised training of text embeddings
07
Ahlmann-Etze C., Barkmann F., Lause J., Boeva F. & Kobak D.
Representation learning of single-cell RNA-seq data
08
Ahlmann-Eltze, C., Barkmann, F., Lause, J., Boeva, V., & Kobak, D.
Representation learning of single-cell RNA-seq data
09
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
10
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.
11
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.
12
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.
13
Müller, S., Koch, L. M., Lensch, H. P., & Berens, P.
Disentangling representations of retinal images with generative models
14
Schmidt, G., Heidrich, H., Berens, P., & Müller, S.
Learning Disease State from Noisy Ordinal Disease Progression Labels.
15
Ofosu Mensah, S., Djoumessi, K., & Berens, P.
Prototype-Guided and Lightweight Adapters for Inherent Interpretation and Generalisation in Federated Learning.
16
Draganov, A., Vadgama, S., Damrich, S., Böhm, J. N., Maes, L., Kobak, D., & Bekkers, E.