Data Science / Neuronal Modeling
We gain insights into the healthy and diseased nervous system by developing using computational models.
01
Holtrup, L., Varghese, J., Schuster, A.K. et al. EyeMatics
EyeMatics—Multicenter data evaluation of real-world data with interoperable medical informatics
02
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.
03
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
04
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
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
Böhm, J. N., Berens, P., & Kobak, D.
Unsupervised visualization of image datasets using contrastive learning
08
Beck, J., Deistler, M., Bernaerts, Y., Macke, J. H., & Berens, P
Efficient identification of informative features in simulation-based inference
09
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
10
Oesterle, J., Krämer, N., Hennig, P., & Berens, P.
Probabilistic solvers enable a straight-forward exploration of numerical uncertainty in neuroscience models
11
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.
12
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.
13
BRAIN Initiative Cell Census Network (BICCN)
A multimodal cell census and atlas of the mammalian primary motor cortex
14
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
15
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
16
Sokoloski, S., Aschner, A., & Coen-Cagli, R.
Modelling the neural code in large populations of correlated neurons
17
18
Schroeder, C., Oesterle, J., Berens, P., Yoshimatsu, T., & Baden, T.
Distinct synaptic transfer functions in same-type photoreceptors.
19
Baden, T., Euler, T., & Berens, P.
Understanding the retinal basis of vision across species.
20
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.
21
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
22
Laturnus, S., von Daranyi, A., Huang, Z., & Berens, P.
MorphoPy: A python package for feature extraction of neural morphologies.
23
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.
24
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.
25
Laturnus, S., Kobak, D., & Berens, P.
A systematic evaluation of interneuron morphology representations for cell type discrimination.
26
Ran, Y., Huang, Z., Baden, T., Schubert, T., Baayen, H., Berens, P., ... & Euler, T.
Type-specific dendritic integration in mouse retinal ganglion cells.
27
Höfling, L., Oesterle, J., Berens, P., & Zeck, G
Probing and predicting ganglion cell responses to smooth electrical stimulation in healthy and blind mouse retina.
28
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.
29
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.
30
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.
31
Schröder, C., James, B., Lagnado, L., & Berens, P.
Approximate bayesian inference for a mechanistic model of vesicle release at a ribbon synapse.
32
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
33
Rogerson, L. E., Zhao, Z., Franke, K., Euler, T., & Berens, P.
Bayesian hypothesis testing and experimental design for two-photon imaging data.
34
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.
35
Dhande, O. S., Stafford, B. K., Franke, K., El-Danaf, R., Percival, K. A., Phan, A. H., ..., Berens, P., ... & Huberman, A. D