In the Department of Data Science we generate knowledge from data to advance neuroscience and ophthalmology.
01
Schröder, C., James, B., Lagnado, L., & Berens, P.
Approximate bayesian inference for a mechanistic model of vesicle release at a ribbon synapse.
02
Kobak, D., & Berens, P.
The art of using t-SNE for single-cell transcriptomics.
03
Kobak, D., Linderman, G., Steinerberger, S., Kluger, Y., & Berens, P.
Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations.
04
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
05
Rogerson, L. E., Zhao, Z., Franke, K., Euler, T., & Berens, P.
Bayesian hypothesis testing and experimental design for two-photon imaging data.
06
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.
07
Berens, P., & Ayhan, M. S.
Proprietary data formats block health research.
08
Dhande, O. S., Stafford, B. K., Franke, K., El-Danaf, R., Percival, K. A., Phan, A. H., ..., Berens, P., ... & Huberman, A. D