Removing inter-experimental variability from functional data in systems neuroscience
Published in Advances in Neural Information Processing Systems (NeurIPS) 34, 2021
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.
Recommended citation: Gonschorek, D., Höfling, L., Szatko, K. P., Franke, K., Schubert, T., Dunn, B., Berens, P., Klindt, D., & Euler, T. (2021). "Removing inter-experimental variability from functional data in systems neuroscience." Advances in Neural Information Processing Systems. 34, 3706-3719.
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