rave_data/README.md

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. The corresponding code for the implementation can be found on GitHub.

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 and can be downloaded in full (incl. raw data) at datadryad

• 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.