We apply machine learning algorithms to enable and accelerate discoveries in neuroscience and ophthalmology, which will ultimately allow us to diagnose diseases earlier and treat them better.

Prof. Dr. Philipp Berens

Prof. Dr. Philipp Berens is Full Professor of Data Science at the University of Tübingen and Director of the Hertie Institute for AI in Brain Health. Also, he is Speaker of the Excellence Cluster “Machine Learning – New Perspectives for Science” and is part of the core faculty of the Tübingen AI Center. His goal is to use machine learning to enable discoveries in basic and clinical neuroscience, with a focus on ophthalmology. He is interested in developing new algorithms whose output can be integrated into scientific or clinical workflows. His work has been recognized with a DFG Heisenberg Professorship, an ERC Starting Grant and the Bernstein Award of the German Ministry for Science and Education.

We apply machine learning algorithms to enable and accelerate discoveries in neuroscience and ophthalmology, which will ultimately allow us to diagnose diseases earlier and treat them better.

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

Weis, M. A., Papadopoulos, S., Hansel, L., Lüddecke, T., Celii, B., Fahey, P. G., ... & Ecker, A. S.

An unsupervised map of excitatory neuron dendritic morphology in the mouse visual cortex

Gervelmeyer, J., Müller, S., Huang, Z., & Berens, P.

Fundus Image Toolbox: A Python package for fundus image processing

Holtrup, L., Varghese, J., Schuster, A.K. et al. EyeMatics

EyeMatics—Multicenter data evaluation of real-world data with interoperable medical informatics

Grote, T., Freiesleben, T. & Berens, P.

Foundation models in healthcare require rethinking reliability

Gervelmeyer, J., Müller, S., Djoumessi, K., Merle, D., Clark, S. J., Koch, L., & Berens, P.

Interpretable-by-design Deep Survival Analysis for Disease Progression Modeling

Lause, J., Berens, P., Kobak, D.

The art of seeing the elephant in the room: 2D embeddings of single-cell data do make sense

Franke, K., Cai, C., Ponder, K., Fu, J., Sokoloski, S., Berens, P., & Tolias, A. S.

Asymmetric distribution of color-opponent response types across mouse visual cortex supports superior color vision in the sky.

Beck, J., Bosch, N., Deistler, M., Kadhim, K.L., Macke, J. H., Hennig, P., Berens, P.

Diffusion Tempering Improves Parameter Estimation with Probabilistic Integrators for Ordinary Differential Equations

Zouridis, I. S., Schmors, L., Fischer, K. M., Berens, P., Preston-Ferrer, P., & Burgalossi, A.

Juxtacellular recordings from identified neurons in the mouse locus coeruleus

Wundram, A.M., Fischer, P., Wunderlich, S., Faber, H., Koch, L.M., Berens, P., Baumgartner C. F.

Leveraging Probabilistic Segmentation Models for Improved Glaucoma Diagnosis: A Clinical Pipeline Approach

Köhler, P., Fadugba, J., Berens, P., Koch, L.M.

Efficiently correcting patch-based segmentation errors to control image-level performance in retinal images

Yaïci, R., Cieplucha, M., Bock, R. et al

ChatGPT und die deutsche Facharztprüfung für Augenheilkunde: eine Evaluierung

Koch, L.M., Baumgartner, C.F. & Berens, P

Distribution shift detection for the postmarket surveillance of medical AI algorithms: a retrospective simulation study

Ayhan, M. S., Neubauer, J., Uzel, M. M., Gelisken, F., & Berens, P.

Interpretable detection of epiretinal membrane from optical coherence tomography with deep neural networks.

González-Márquez, R., Schmidt, L., Schmidt, B. M., Berens, P., & Kobak, D.

The landscape of biomedical research

Grote, T., Berens, P.

A paradigm shift?—On the ethics of medical large language models

Ayhan, M. S., Faber, H., Kühlewein, L., Inhoffen, W., Aliyeva, G., Ziemssen, F., & Berens, P.

Multitask Learning for Activity Detection in Neovascular Age-Related Macular Degeneration

Djoumessi, K. R. D., Ilanchezian, I., Kühlewein, L., Faber, H., Baumgartner, C. F., Bah, B., Berens, P. & Koch, L. M.

Sparse Activations for Interpretable Disease Grading

Grote, T., & Berens, P

Uncertainty, evidence, and the integration of machine learning into medical practice

Congiu, M., Mondoloni, S., Zouridis, I. S., Schmors, L., Lecca, S., Lalive, A. L., Ginggen, K., Deng, F., Berens, P., Paolicelli, R. C., Li, Y., Burgalossi, A. & Mameli, M.

Plasticity of neuronal dynamics in the lateral habenula for cue-punishment associative learning

Böhm, J. N., Berens, P., & Kobak, D.

Unsupervised visualization of image datasets using contrastive learning

Janschewski, J., Käppler, C., & Berens, P.

School predictors of mental health problems in children and adolescents based on a survey of students in hospital and regular schools

Boreiko, V., Augustin, M., Croce, F., Berens, P., & Hein, M.

Sparse visual counterfactual explanations in image space

Koch, L. M., Schürch, C. M., Gretton, A., & Berens, P.

Hidden in plain sight: Subgroup shifts escape ood detection

Beck, J., Deistler, M., Bernaerts, Y., Macke, J. H., & Berens, P

Efficient identification of informative features in simulation-based inference

Grote, T., & Berens, P.

How competitors become collaborators—Bridging the gap (s) between machine learning algorithms and clinicians

Böhm, J. N., Berens, P., & Kobak, D.

Attraction-repulsion spectrum in neighbor embeddings

Blum, C., Baur, D., Achauer, L. C., Berens, P., [...], Huang, Z., [...] , Macke, J.H., [...] & Ziemann, U.

Personalized neurorehabilitative precision medicine: from data to therapies (MWKNeuroReha)–a multi-centre prospective observational clinical trial to predict long-term outcome of patients with acute motor stroke

Strauss, S., Korympidou, M. M., Ran, Y., Franke, K., Schubert, T., Baden, T., Berens, P., Euler, T. & Vlasits, A. L

Center-surround interactions underlie bipolar cell motion sensitivity in the mouse retina

Boreiko, V., Ilanchezian, I., Ayhan, M. S., Müller, S., Koch, L. M., Faber, H., Berens, P. & Hein, M.

Visual explanations for the detection of diabetic retinopathy from retinal fundus images

Oesterle, J., Krämer, N., Hennig, P., & Berens, P.

Probabilistic solvers enable a straight-forward exploration of numerical uncertainty in neuroscience models

Faber, H., Berens, P., & Rohrbach, J. M.

Ocular changes as a diagnostic tool for malaria

Ayhan, M. S., Kümmerle, L. B., Kühlewein, L., Inhoffen, W., Aliyeva, G., Ziemssen, F., & Berens, P.

Clinical validation of saliency maps for understanding deep neural networks in ophthalmology

Behrens, C., Yadav, S. C., Korympidou, M. M., Zhang, Y., Haverkamp, S., Irsen, S., ... & Schubert, T.

Retinal horizontal cells use different synaptic sites for global feedforward and local feedback signaling.

Gonschorek, D., Höfling, L., Szatko, K. P., Franke, K., Schubert, T., Dunn, B., Berens, P. ... & Euler, T.

Removing inter-experimental variability from functional data in systems neuroscience.

BRAIN Initiative Cell Census Network (BICCN)

A multimodal cell census and atlas of the mammalian primary motor cortex

Yoshimatsu, T., Bartel, P., Schröder, C., Janiak, F. K., St-Pierre, F., Berens, P., & Baden, T.

Ancestral circuits for vertebrate color vision emerge at the first retinal synapse

Scala, F., Kobak, D., Bernabucci, M., Bernaerts, Y., Cadwell, C. R., Castro, J. R., [...], Berens, P. & Tolias, A. S.

Phenotypic variation of transcriptomic cell types in mouse motor cortex

Ilanchezian, I., Kobak, D., Faber, H., Ziemssen, F., Berens, P., & Ayhan, M. S

Interpretable gender classification from retinal fundus images using BagNets.

Lause, J., Berens, P., & Kobak, D.

Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data.

Huang, Z., Ran, Y., Oesterle, Y., Euler, T., Berens, P.

Estimating smooth and sparse neural receptive fields with a flexible spline basis

Kobak, D., Bernaerts, Y., Weis, M. A., Scala, F., Tolias, A. S., & Berens, P.

Sparse reduced-rank regression for exploratory visualisation of paired multivariate data

Laturnus, S., & Berens, P.

Morphvae: Generating neural morphologies from 3d-walks using a variational autoencoder with spherical latent space.

Schroeder, C., Oesterle, J., Berens, P., Yoshimatsu, T., & Baden, T.

Distinct synaptic transfer functions in same-type photoreceptors.

Baden, T., Euler, T., & Berens, P.

Understanding the retinal basis of vision across species.

Schröder, C., Klindt, D., Strauss, S., Franke, K., Bethge, M., Euler, T., & Berens, P.

System identification with biophysical constraints: A circuit model of the inner retina.

Oesterle, J., Behrens, C., Schröder, C., Hermann, T., Euler, T., Franke, K., ... & Berens, P.

Bayesian inference for biophysical neuron models enables stimulus optimization for retinal neuroprosthetics

Pfau, M., Walther, G., von der Emde, L., Berens, P., Faes, L., Fleckenstein, M., ... & Holz, F. G.

Artificial intelligence in ophthalmology: Guidelines for physicians for the critical evaluation of studies.

Ayhan, M. S., Kühlewein, L., Aliyeva, G., Inhoffen, W., Ziemssen, F., & Berens, P.

Expert-validated estimation of diagnostic uncertainty for deep neural networks in diabetic retinopathy detection.

Laturnus, S., von Daranyi, A., Huang, Z., & Berens, P.

MorphoPy: A python package for feature extraction of neural morphologies.

Yoshimatsu, T., Schröder, C., Nevala, N. E., Berens, P., & Baden, T.

Fovea-like photoreceptor specializations underlie single UV cone driven prey-capture behavior in zebrafish.

Szatko, K. P., Korympidou, M. M., Ran, Y., Berens, P., Dalkara, D., Schubert, T., ... & Franke, K.

Neural circuits in the mouse retina support color vision in the upper visual field.

Meding, K., Bruijns, S. A., Schölkopf, B., Berens, P., & Wichmann, F. A.

Phenomenal causality and sensory realism.

Laturnus, S., Kobak, D., & Berens, P.

A systematic evaluation of interneuron morphology representations for cell type discrimination.

Ran, Y., Huang, Z., Baden, T., Schubert, T., Baayen, H., Berens, P., ... & Euler, T.

Type-specific dendritic integration in mouse retinal ganglion cells.

Höfling, L., Oesterle, J., Berens, P., & Zeck, G

Probing and predicting ganglion cell responses to smooth electrical stimulation in healthy and blind mouse retina.

Zhao, Z., Klindt, D. A., Maia Chagas, A., Szatko, K. P., Rogerson, L., Protti, D. A., ... & Euler, T.

The temporal structure of the inner retina at a single glance.

Power, M. J., Rogerson, L. E., Schubert, T., Berens, P., Euler, T., & Paquet‐Durand, F.

Systematic spatiotemporal mapping reveals divergent cell death pathways in three mouse models of hereditary retinal degeneration.

Cadwell, C. R., Scala, F., Fahey, P. G., Kobak, D., Mulherkar, S., Sinz, F. H., ... & Tolias, A. S.