Understanding the eye through data — from single synapses to population-scale disease.
01
Froudarakis, E., Cohen, U., Diamantaki, M., Patel, S., Tan, Z., Muhammad, T., ... & Tolias, A. S.
Object manifold geometry across the mouse cortical visual hierarchy
02
González-Márquez, R., Berens, P., & Kobak
Cropping outperforms dropout as an augmentation strategy for self-supervised training of text embeddings
03
Ahlmann-Eltze, C., Barkmann, F., Lause, J., Boeva, V., & Kobak, D.
Representation learning of single-cell RNA-seq data
04
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
05
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.
06
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.
07
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.
08
Müller, S., Koch, L. M., Lensch, H. P., & Berens, P.
Disentangling representations of retinal images with generative models
09
Schmidt, G., Heidrich, H., Berens, P., & Müller, S.
Learning Disease State from Noisy Ordinal Disease Progression Labels.
10
Ofosu Mensah, S., Djoumessi, K., & Berens, P.
Prototype-Guided and Lightweight Adapters for Inherent Interpretation and Generalisation in Federated Learning.
11
Draganov, A., Vadgama, S., Damrich, S., Böhm, J. N., Maes, L., Kobak, D., & Bekkers, E.
On the Importance of Embedding Norms in Self-Supervised Learning.
12
Kobak, D., González-Márquez, R., Horvát, E. Á., & Lause, J.
Delving into LLM-assisted writing in biomedical publications through excess vocabulary
13
Schmors, L., Kotkat, A. H., Bauer, Y., Huang, Z., Crombie, D., Meyerolbersleben, L. S., ... & Busse, L.
Effects of corticothalamic feedback depend on visual responsiveness and stimulus type.
14
Ilanchezian, I., Boreiko, V., Kühlewein, L., Huang, Z., Seçkin Ayhan, M., Hein, M., ... & Berens, P.
Development and validation of an AI algorithm to generate realistic and meaningful counterfactuals for retinal imaging based on diffusion models.
15
Djoumessi, K., Huang, Z., Kühlewein, L., Rickmann, A., Simon, N., Koch, L. M., & Berens, P.
An inherently interpretable AI model improves screening speed and accuracy for early diabetic retinopathy
16
Oesterle, J., Ran, Y., Stahr, P., Kerr, J. N., Schubert, T., Berens, P., & Euler, T