Data Science for Vision Research
We use techniques from machine learning, high-dimensional statistics, neural coding, visualization and data management to make use of large and complex datasets in visual neuroscience, with a particular focus on the retina. Specifically, we aim to link the computation performed by individual cell types in the visual system to their underlying genetics, biophysics, anatomy and connectivity. Our model systems are the circuits of the early visual system with a focus on the retina and the primary visual cortex of mice and corresponding structures in the zebrafish visual system. We collaborate closely with our experimental partners to answer fundamental questions about how visual information is processed in these structures. We also work on improving AI-based algorithms for diagnosing ophthalmological diseases.
- Kobak, D., & Berens, P. (2019). The art of using t-SNE for single-cell transcriptomics. Nature communications, 10(1), 1-14. link
- Baden, T., Euler, T., & Berens, P. (2019). Understanding the retinal basis of vision across species. Nature Reviews Neuroscience, 1-16. link
- Bellet M. E., Bellet J., Nienborg H., Hafed Z. M., Berens P. "Human-level saccade detection performance using deep neural networks" (2019) Journal of Neurophysiology 121:2, 646-661, link
- Leibig, C., Allken, V., Ayhan, M. S., Berens, P., & Wahl, S. (2017). Leveraging uncertainty information from deep neural networks for disease detection. Scientific reports, 7(1), 17816, link
- Franke* K., P. Berens*, T. Schubert, M. Bethge, T. Euler, T. Baden (2017): Inhibition decorrelates visual feature representation in the inner retina. Nature, 542, 439-444 link
- Baden T*, P Berens*, K Franke*, M Rezac, M Bethge, T Euler$: The functional diversity of retinal ganglion cells in the mouse (2016), Nature,529, 345-350, link