Computational & Systems Neuroscience

Dominic Gonschorek

I study how neural circuits encode the visual world, and how that encoding changes with context. My work centres on the mouse retina and its projections to the superior colliculus. I combine large-scale functional recordings with machine learning models of neural activity.

I am a postdoctoral researcher in the Euler Lab at the University of Tübingen. Besides running experiments, I develop the computational side of the work: methods that integrate data across experiments, models that predict neural responses, representation learning for neural time series, and curated datasets and software that other labs can reuse.

  • Two-photon imaging
  • Multi-electrode array recordings
  • Deep learning models of neural responses
  • Self-supervised representation learning
  • Large-scale data integration
  • Python

Research themes

Visual coding & cell types

How the retina's parallel output channels encode visual input, and how these signals are organized in the superior colliculus.

Neuromodulation & context

How neuromodulators such as nitric oxide and histamine reshape retinal computations depending on brain state and context.

Machine learning for neuroscience

Predictive models of neural responses, contrastive learning for multi-trial recordings, and removal of variability between experiments.

Open datasets & tools

Curated large-scale datasets, shared modelling frameworks and connectomic resources that make results reusable across labs.

Selected work

  1. The functional organization of retinal input to the mouse superior colliculus

    Qiu, Y.*, Schmors, L.*, Zhou, N.*, Gonschorek, D.*, Akhmetali, M., Smith, C., Sumser, A., Vallens, M., Cadwell, C. R., Gabbiani, F., Joesch, M., Tolias, A., Berens, P., Euler, T., Sinz, F., Reimer, J., Franke, K.

    Current Biology In press Shared first author

  2. A large-scale dataset of functional mouse ganglion cell layer responses

    Gonschorek, D.*, Oesterle, J.*, Zenkel, T.*, D'Agostino, F.*, Cai, C., Dyszkant, N., Szatko, K. P., Deja, F., Schwerd-Kleine, T., Arlinghaus, R., Franke, K., Zhao, Z., Schubert, T., Berens, P., Euler, T.

    Scientific Data, 13, 1170 Shared first author

  3. The openretina Project: Collaborative Retina Modelling Across Datasets and Species

    D'Agostino, F.*, Zenkel, T.*, Lorenzi, B., Vystrčilová, M., Gonschorek, D., Suhai, S., Virgili, S., Ecker, A. S., Marre, O., Höfling, L.§, Euler, T.§, Bethge, M.§

    eLife, RP111328 Reviewed Preprint

  4. TRACE: Contrastive learning for multi-trial time series data in neuroscience

    Schmors, L., Gonschorek, D., Böhm, J. N., Qiu, Y., Zhou, N., Kobak, D., Tolias, A., Sinz, F., Reimer, J., Franke, K., Damrich, S., Berens, P.

    Advances in Neural Information Processing Systems 38 (NeurIPS 2025), 79837–79866

  5. Nitric oxide modulates contrast suppression in a subset of mouse retinal ganglion cells

    Gonschorek, D., Goldin, M. A., Oesterle, J., Schwerd-Kleine, T., Arlinghaus, R., Zhao, Z., Schubert, T., Marre, O., Euler, T.

    eLife, 13, RP98742 First author

  6. Removing inter-experimental variability from functional data in systems neuroscience

    Gonschorek, D.*, Höfling, L.*, Szatko, K. P., Franke, K., Schubert, T., Dunn, B., Berens, P., Klindt, D. A.§, Euler, T.§

    Advances in Neural Information Processing Systems 34 (NeurIPS 2021), 3706–3719 Spotlight Shared first author

All publications →

News

  • Our paper on the functional organization of retinal input to the mouse superior colliculus has been accepted in Current Biology. Imaging more than 200,000 retinal ganglion cell axon boutons in vivo and combining the data with deep learning models, we find that nearly the full diversity of retinal ganglion cell types reaches the colliculus, organized in systematic functional gradients. Preprint · Code
  • Our All-GCL dataset paper is out in Scientific Data: two-photon recordings from more than 80,000 neurons in the mouse ganglion cell layer, with standardized metadata, pretrained cell-type classifiers, tutorials and analysis code. Paper · Code · Dataset
  • The openretina Project is now an eLife Reviewed Preprint. openretina is an open-source Python package for training, evaluating and comparing deep learning models of the retina across datasets and species. Code
  • Presented a poster on our superior colliculus project at the FASEB Retinal Neurobiology and Visual Processing Conference in Sacramento, California, supported by a FASEB travel grant.

All news →

Contact

I am happy to hear from researchers interested in collaborating, students looking for projects, and anyone curious about vision and the brain. The best way to reach me is by email at dominic.gonschorek@cin.uni-tuebingen.de. You can also find me on Google Scholar, GitHub and LinkedIn.