# Data: Removing Inter-Experimental Variability from Functional Data in Systems Neuroscience This repository hosts the data of [Gonschorek, Höfling et al. (2021), *Removing Inter-Experimental Variability from Functional Data in Systems Neuroscience*, NeurIPS 2021][1]. The corresponding code for the implementation can be found on [GitHub][2]. The files contain the following data and information: * bio/bc_dataset_A (pickle): * This file contains the preprocessed bipolar cell data in response to the local and full-field chirp, their IPL (inner plexiform layer) depths and cell type labels obtained from Franke, Berens et al. (2017), *Inhibition decorrelates visual feature representation in the inner retina*, Nature. This dataset is licensed under the [CC0 1.0 Universal (CC0 1.0) Public Domain Dedication][3] and can be downloaded in full (incl. raw data) at [datadryad][4] * bio/bc_dataset_B (pickle) * This file contains the preprocessed bipolar cell data in response to the local and full-field chirp and their IPL depths obtained from Zhao, Klindt et al. (2020), *The temporal structure of the inner retina at a single glance*, Scientific Reports. * ipl/BC_Profiles_Helmstaedter (txt) * This file contains electron-microscopy (EM) data of axonal stratification profiles of bipolar cell types. * array of shape 400x14, array[d, t] = p(IPL depth = d|BC type = t) * ipl/ipl (mat) * Matlab file containing a dictionary with keys "d" and "prior"; d: 400x1 array, IPL depth samples; prior: 400 x 14 array, with array[d, t] = p(BC type = t|IPL depth = d). * silico/sim_dataset (pickle) * This file contains the dataset object of bipolar cell responses to the chirp stimulus simulated using the model described in [Schröder, Klindt et al. (2020), *System Identification with Biophysical Constraints: A Circuit Model of the Inner Retina*, NeurIPS 2020][5]. [1]: https://www.biorxiv.org/content/10.1101/2021.10.29.466492v2 "Gonschorek, Höfling et al. (2021)" [2]: https://github.com/eulerlab/rave "rave" [3]: https://creativecommons.org/publicdomain/zero/1.0/ "CC0 1.0 Universal (CC0 1.0) Public Domain Dedication" [4]: https://datadryad.org/stash/dataset/doi:10.5061/dryad.rs2qp "datadryad" [5]: https://proceedings.neurips.cc/paper/2020/file/b139e104214a08ae3f2ebcce149cdf6e-Paper.pdf "Schröder, Klindt et al. (2020)"