Publications Conference papers
Removing inter-experimental variability from functional data in systems neuroscience
Advances in Neural Information Processing Systems 34 (NeurIPS 2021), 3706–3719 Spotlight
Shared first author
Summary
Integrating data from multiple experiments is common practice in systems neuroscience, but it requires inter-experimental variability to be negligible compared to the biological signal of interest. This work presents a framework using adversarial optimization to remove such inter-experimental variability, tested on two-photon imaging recordings of retinal bipolar cells.
Citation
Gonschorek, D., Höfling, L., Szatko, K. P., Franke, K., Schubert, T., Dunn, B., Berens, P., Klindt, D. A., Euler, T. (2021). Removing inter-experimental variability from functional data in systems neuroscience. Advances in Neural Information Processing Systems 34 (NeurIPS 2021), 3706–3719.
* shared first authorship§ shared senior authorship