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.
01
Holtrup, L., Varghese, J., Schuster, A.K. et al. EyeMatics
EyeMatics—Multicenter data evaluation of real-world data with interoperable medical informatics
02
Grote, T., Freiesleben, T. & Berens, P.
Foundation models in healthcare require rethinking reliability
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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
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Lause, J., Berens, P., Kobak, D.
The art of seeing the elephant in the room: 2D embeddings of single-cell data do make sense
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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.
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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
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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
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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
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Köhler, P., Fadugba, J., Berens, P., Koch, L.M.
Efficiently correcting patch-based segmentation errors to control image-level performance in retinal images
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Yaïci, R., Cieplucha, M., Bock, R. et al
ChatGPT und die deutsche Facharztprüfung für Augenheilkunde: eine Evaluierung
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Koch, L.M., Baumgartner, C.F. & Berens, P
Distribution shift detection for the postmarket surveillance of medical AI algorithms: a retrospective simulation study
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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.
13
González-Márquez, R., Schmidt, L., Schmidt, B. M., Berens, P., & Kobak, D.
The landscape of biomedical research
14
Grote, T., Berens, P.
A paradigm shift?—On the ethics of medical large language models
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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
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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
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Grote, T., & Berens, P
Uncertainty, evidence, and the integration of machine learning into medical practice
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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
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Böhm, J. N., Berens, P., & Kobak, D.
Unsupervised visualization of image datasets using contrastive learning
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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
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Boreiko, V., Augustin, M., Croce, F., Berens, P., & Hein, M.
Sparse visual counterfactual explanations in image space
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Koch, L. M., Schürch, C. M., Gretton, A., & Berens, P.
Hidden in plain sight: Subgroup shifts escape ood detection
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Beck, J., Deistler, M., Bernaerts, Y., Macke, J. H., & Berens, P
Efficient identification of informative features in simulation-based inference
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Böhm, J. N., Berens, P., & Kobak, D.
Attraction-repulsion spectrum in neighbor embeddings
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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
27
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
28
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
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Oesterle, J., Krämer, N., Hennig, P., & Berens, P.
Probabilistic solvers enable a straight-forward exploration of numerical uncertainty in neuroscience models
30
Faber, H., Berens, P., & Rohrbach, J. M.
Ocular changes as a diagnostic tool for malaria
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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
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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.
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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.
34
BRAIN Initiative Cell Census Network (BICCN)
A multimodal cell census and atlas of the mammalian primary motor cortex
35
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
36
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
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Ilanchezian, I., Kobak, D., Faber, H., Ziemssen, F., Berens, P., & Ayhan, M. S
Interpretable gender classification from retinal fundus images using BagNets.
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Lause, J., Berens, P., & Kobak, D.
Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data.
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Huang, Z., Ran, Y., Oesterle, Y., Euler, T., Berens, P.
Estimating smooth and sparse neural receptive fields with a flexible spline basis
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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
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Schroeder, C., Oesterle, J., Berens, P., Yoshimatsu, T., & Baden, T.
Distinct synaptic transfer functions in same-type photoreceptors.
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Baden, T., Euler, T., & Berens, P.
Understanding the retinal basis of vision across species.
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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.
45
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
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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.
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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.
48
Laturnus, S., von Daranyi, A., Huang, Z., & Berens, P.
MorphoPy: A python package for feature extraction of neural morphologies.
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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.
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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.
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Meding, K., Bruijns, S. A., Schölkopf, B., Berens, P., & Wichmann, F. A.
Phenomenal causality and sensory realism.
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Laturnus, S., Kobak, D., & Berens, P.
A systematic evaluation of interneuron morphology representations for cell type discrimination.
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Ran, Y., Huang, Z., Baden, T., Schubert, T., Baayen, H., Berens, P., ... & Euler, T.
Type-specific dendritic integration in mouse retinal ganglion cells.
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Höfling, L., Oesterle, J., Berens, P., & Zeck, G
Probing and predicting ganglion cell responses to smooth electrical stimulation in healthy and blind mouse retina.
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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.
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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.
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Cadwell, C. R., Scala, F., Fahey, P. G., Kobak, D., Mulherkar, S., Sinz, F. H., ... & Tolias, A. S.
Cell type composition and circuit organization of clonally related excitatory neurons in the juvenile mouse neocortex.
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Berens, P., Waldstein, S. M., Ayhan, M. S., Kuemmerle, L., Agostini, H., Stahl, A., & Ziemssen, F.
Potential of methods of artificial intelligence for quality assurance.
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Grote, T., & Berens, P.
On the ethics of algorithmic decision-making in healthcare.
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Schröder, C., James, B., Lagnado, L., & Berens, P.
Approximate bayesian inference for a mechanistic model of vesicle release at a ribbon synapse.
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Kobak, D., & Berens, P.
The art of using t-SNE for single-cell transcriptomics.
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Kobak, D., Linderman, G., Steinerberger, S., Kluger, Y., & Berens, P.
Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations.
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Scala, F., Kobak, D., Shan, S., Bernaerts, Y., Laturnus, S., Cadwell, C. R., ... Berens, P., ... & Tolias, A. S
Layer 4 of mouse neocortex differs in cell types and circuit organization between sensory areas
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Rogerson, L. E., Zhao, Z., Franke, K., Euler, T., & Berens, P.
Bayesian hypothesis testing and experimental design for two-photon imaging data.
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Rosón, M. R., Bauer, Y., Kotkat, A. H., Berens, P., Euler, T., & Busse, L.
Mouse dLGN receives functional input from a diverse population of retinal ganglion cells with limited convergence.
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Bellet, M. E., Bellet, J., Nienborg, H., Hafed, Z. M., & Berens, P
Human-level saccade detection performance using deep neural networks.
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Berens, P., & Ayhan, M. S.
Proprietary data formats block health research.
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Dhande, O. S., Stafford, B. K., Franke, K., El-Danaf, R., Percival, K. A., Phan, A. H., ..., Berens, P., ... & Huberman, A. D