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
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.
02
González-Márquez, R., Schmidt, L., Schmidt, B. M., Berens, P., & Kobak, D.
The landscape of biomedical research
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
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
05
Grote, T., & Berens, P
Uncertainty, evidence, and the integration of machine learning into medical practice
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
07
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.
08
Böhm, J. N., Berens, P., & Kobak, D.
Unsupervised visualization of image datasets using contrastive learning
09
Koch, L. M., Schürch, C. M., Gretton, A., & Berens, P.
Hidden in Plain Sight: Subgroup Shifts Escape OOD Detection
10
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
11
Boreiko, V., Augustin, M., Croce, F., Berens, P., & Hein, M.
Sparse visual counterfactual explanations in image space
12
Koch, L. M., Schürch, C. M., Gretton, A., & Berens, P.
Hidden in plain sight: Subgroup shifts escape ood detection
13
Beck, J., Deistler, M., Bernaerts, Y., Macke, J. H., & Berens, P
Efficient identification of informative features in simulation-based inference
14
15
Böhm, J. N., Berens, P., & Kobak, D.
Attraction-repulsion spectrum in neighbor embeddings
16
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
17
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
18
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
19
Oesterle, J., Krämer, N., Hennig, P., & Berens, P.
Probabilistic solvers enable a straight-forward exploration of numerical uncertainty in neuroscience models
20
Faber, H., Berens, P., & Rohrbach, J. M.
Ocular changes as a diagnostic tool for malaria
21
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
22
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.
23
Böhm, J. N., Berens, P., & Kobak, D.
Attraction-repulsion spectrum in neighbor embeddings
24
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.
25
BRAIN Initiative Cell Census Network (BICCN)
A multimodal cell census and atlas of the mammalian primary motor cortex
26
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
27
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
28
Ilanchezian, I., Kobak, D., Faber, H., Ziemssen, F., Berens, P., & Ayhan, M. S
Interpretable gender classification from retinal fundus images using BagNets.
29
Lause, J., Berens, P., & Kobak, D.
Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data.
30
Huang, Z., Ran, Y., Oesterle, Y., Euler, T., Berens, P.
Estimating smooth and sparse neural receptive fields with a flexible spline basis
31
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
32
33
Schroeder, C., Oesterle, J., Berens, P., Yoshimatsu, T., & Baden, T.
Distinct synaptic transfer functions in same-type photoreceptors.
34
Baden, T., Euler, T., & Berens, P.
Understanding the retinal basis of vision across species.
35
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.
36
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
37
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.
38
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.
39
Laturnus, S., von Daranyi, A., Huang, Z., & Berens, P.
MorphoPy: A python package for feature extraction of neural morphologies.
40
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.
41
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.
42
Meding, K., Bruijns, S. A., Schölkopf, B., Berens, P., & Wichmann, F. A.
Phenomenal causality and sensory realism.
43
Laturnus, S., Kobak, D., & Berens, P.
A systematic evaluation of interneuron morphology representations for cell type discrimination.
44
Ran, Y., Huang, Z., Baden, T., Schubert, T., Baayen, H., Berens, P., ... & Euler, T.
Type-specific dendritic integration in mouse retinal ganglion cells.
45
Höfling, L., Oesterle, J., Berens, P., & Zeck, G
Probing and predicting ganglion cell responses to smooth electrical stimulation in healthy and blind mouse retina.
46
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.
47
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.
48
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.
49
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.
50
Grote, T., & Berens, P.
On the ethics of algorithmic decision-making in healthcare.
51
Schröder, C., James, B., Lagnado, L., & Berens, P.
Approximate bayesian inference for a mechanistic model of vesicle release at a ribbon synapse.
52
Kobak, D., & Berens, P.
The art of using t-SNE for single-cell transcriptomics.
53
Kobak, D., Linderman, G., Steinerberger, S., Kluger, Y., & Berens, P.
Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations.
54
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
55
Rogerson, L. E., Zhao, Z., Franke, K., Euler, T., & Berens, P.
Bayesian hypothesis testing and experimental design for two-photon imaging data.
56
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.
57
Bellet, M. E., Bellet, J., Nienborg, H., Hafed, Z. M., & Berens, P
Human-level saccade detection performance using deep neural networks.
58
Berens, P., & Ayhan, M. S.
Proprietary data formats block health research.
59
Dhande, O. S., Stafford, B. K., Franke, K., El-Danaf, R., Percival, K. A., Phan, A. H., ..., Berens, P., ... & Huberman, A. D