Prof. Dr. Philipp Berens

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
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.

Apr 11, 2024 | Scientific Reports, 14(1), 8484.
02
González-Márquez, R., Schmidt, L., Schmidt, B. M., Berens, P., & Kobak, D.

The landscape of biomedical research

Apr 09, 2024 | Patterns, 100968
03
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

Jul 21, 2023 | Translational Vision Science & Technology, 12(4), 12-12
04
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

Jul 20, 2023 | Proceedings of Medical Imaging with Deep Learning (MIDL)
05
Grote, T., & Berens, P

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

Jul 19, 2023 | The Journal of Medicine and Philosophy: A Forum for Bioethics and Philosophy of Medicine (Vol. 48, No. 1, pp. 84-97). US: Oxford University Press
06
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

Jul 12, 2023 | Molecular Psychiatry, 1-10
07
Böhm, J. N., Berens, P., & Kobak, D.

Unsupervised visualization of image datasets using contrastive learning

May 30, 2023 | Proceedings of the International Conference on Learning Representations (ICLR)
08
Koch, L. M., Schürch, C. M., Gretton, A., & Berens, P.

Hidden in Plain Sight: Subgroup Shifts Escape OOD Detection

May 16, 2023 | In Medical Imaging with Deep Learning.
09
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

Jan 10, 2023 | Zeitschrift für Pädagogische Psychologie
10
Boreiko, V., Augustin, M., Croce, F., Berens, P., & Hein, M.

Sparse visual counterfactual explanations in image space

Sep 09, 2022 | DAGM German Conference on Pattern Recognition (pp. 133-148)
11
Koch, L. M., Schürch, C. M., Gretton, A., & Berens, P.

Hidden in plain sight: Subgroup shifts escape ood detection

Jul 01, 2022 | Proceedings of Medical Imaging with Deep Learning (MIDL)
12
Beck, J., Deistler, M., Bernaerts, Y., Macke, J. H., & Berens, P

Efficient identification of informative features in simulation-based inference

Feb 24, 2022 | Advances in Neural Information Processing Systems, 35, 19260-19273
14
Böhm, J. N., Berens, P., & Kobak, D.

Attraction-repulsion spectrum in neighbor embeddings

Feb 21, 2022 | The Journal of Machine Learning Research, 23(1), 95, 4118–4149
15
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

Feb 14, 2022 | BMC Neurology, 22(1), 1-15
16
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

Feb 07, 2022 | Nature Communications, 13(1), 5574
17
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

Feb 02, 2022 | International conference on medical image computing and computer-assisted intervention (MICCAI) (pp. 539-549), Cham: Springer Nature Switzerland
18
Oesterle, J., Krämer, N., Hennig, P., & Berens, P.

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

Jan 04, 2022 | Journal of Computational Neuroscience, 50(4), 485-503
19
Faber, H., Berens, P., & Rohrbach, J. M.

Ocular changes as a diagnostic tool for malaria

Jan 02, 2022 | Der Ophthalmologe, 1-6
20
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

Jan 01, 2022 | Medical Image Analysis, 77, 102364
21
Behrens, C., Yadav, S. C., Korympidou, M. M., Zhang, Y., Haverkamp, S., Berens, P. & Schubert, T.

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

Jan 01, 2022 | Current Biology, 32(3), 545-558
22
Böhm, J. N., Berens, P., & Kobak, D.

Attraction-repulsion spectrum in neighbor embeddings

Jan 01, 2022 | The Journal of Machine Learning Research, 23(1), 4118-4149.
23
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.

Dec 06, 2021 | Advances in Neural Information Processing Systems, 34, 3706-3719.
24
BRAIN Initiative Cell Census Network (BICCN)

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

Oct 21, 2021 | Nature, 598(7879), 86-102.
25
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

Oct 13, 2021 | Science Advances, 7(42), eabj6815
26
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

Oct 07, 2021 | Nature, 598(7879), 144-150
27
Ilanchezian, I., Kobak, D., Faber, H., Ziemssen, F., Berens, P., & Ayhan, M. S

Interpretable gender classification from retinal fundus images using BagNets.

Sep 21, 2021 | Proceedings, Part III 24 (pp. 477-487). Springer International Publishing.
28
Lause, J., Berens, P., & Kobak, D.

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

Sep 09, 2021 | Genome biology, 22(1), 1-20
29
Huang, Z., Ran, Y., Oesterle, Y., Euler, T., Berens, P.

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

Aug 18, 2021 | Neurons, Behavior, Data analysis, and Theory, Vol. 5, Issue 3, 2021
30
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

Aug 07, 2021 | Journal of the Royal Statistical Society Series C: Applied Statistics, 70(4), 980-1000
32
Schroeder, C., Oesterle, J., Berens, P., Yoshimatsu, T., & Baden, T.

Distinct synaptic transfer functions in same-type photoreceptors.

Jul 16, 2021 | Elife, 10, e67851.
33
Baden, T., Euler, T., & Berens, P.

Understanding the retinal basis of vision across species.

Dec 12, 2020 | Nature Reviews Neuroscience, 21(1), 5-20.
34
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.

Dec 08, 2020 | Advances in Neural Information Processing Systems, 33, 15439-15450.
35
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

Oct 27, 2020 | Elife, 9, e54997.
36
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.

Aug 28, 2020 | Der Ophthalmologe, 117, 973-988.
37
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.

Aug 20, 2020 | Medical image analysis, 64, 101724.
38
Laturnus, S., von Daranyi, A., Huang, Z., & Berens, P.

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

Aug 03, 2020 | Journal of Open Source Software, 5(52), 2339.
39
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.

Jul 22, 2020 | Neuron, 107(2), 320-337.
40
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.

Jul 13, 2020 | Nature communications, 11(1), 3481.
41
Meding, K., Bruijns, S. A., Schölkopf, B., Berens, P., & Wichmann, F. A.

Phenomenal causality and sensory realism.

Jun 01, 2020 | i-Perception, 11(3), 2041669520927038.
42
Laturnus, S., Kobak, D., & Berens, P.

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

May 04, 2020 | Neuroinformatics, 18, 591-609.
43
Ran, Y., Huang, Z., Baden, T., Schubert, T., Baayen, H., Berens, P., ... & Euler, T.

Type-specific dendritic integration in mouse retinal ganglion cells.

Apr 30, 2020 | Nature Communications, 11(1), 2101.
44
Höfling, L., Oesterle, J., Berens, P., & Zeck, G

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

Mar 23, 2020 | Scientific reports, 10(1), 5248.
45
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.

Mar 10, 2020 | Scientific reports, 10(1), 4399.
46
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.

Mar 05, 2020 | Journal of Comparative Neurology, 528(7), 1113-1139.
47
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.

Mar 05, 2020 | Elife, 9, e52951.
48
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.

Feb 24, 2020 | Der Ophthalmologe, 117, 320-325.
49
Grote, T., & Berens, P.

On the ethics of algorithmic decision-making in healthcare.

Feb 20, 2020 | Journal of medical ethics, 46(3), 205-211.
50
Schröder, C., James, B., Lagnado, L., & Berens, P.

Approximate bayesian inference for a mechanistic model of vesicle release at a ribbon synapse.

Dec 10, 2019 | Advances in Neural Information Processing Systems, 32.
51
Kobak, D., & Berens, P.

The art of using t-SNE for single-cell transcriptomics.

Nov 28, 2019 | Nature communications, 10(1), 5416.
52
Kobak, D., Linderman, G., Steinerberger, S., Kluger, Y., & Berens, P.

Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations.

Sep 16, 2019 | Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Cham: Springer International Publishing, 2019.
53
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

Sep 13, 2019 | Nature communications, 10(1), 4174.
54
Rogerson, L. E., Zhao, Z., Franke, K., Euler, T., & Berens, P.

Bayesian hypothesis testing and experimental design for two-photon imaging data.

Aug 22, 2019 | PLoS computational biology, 15(8), e1007205.
55
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.

Apr 17, 2019 | Neuron, 102(2), 462-476.
56
Bellet, M. E., Bellet, J., Nienborg, H., Hafed, Z. M., & Berens, P

Human-level saccade detection performance using deep neural networks.

Feb 07, 2019 | Journal of neurophysiology, 121(2), 646-661.
57
Berens, P., & Ayhan, M. S.

Proprietary data formats block health research.

Jan 31, 2019 | Nature, 565(7737), 429-430.
58
Dhande, O. S., Stafford, B. K., Franke, K., El-Danaf, R., Percival, K. A., Phan, A. H., ..., Berens, P., ... & Huberman, A. D

Molecular fingerprinting of on–off direction-selective retinal ganglion cells across species and relevance to primate visual circuits.

Jan 02, 2019 | Journal of Neuroscience, 39(1), 78-95.