Published November 2, 2020
| Version Version 1.0
Dataset
Open
Data for "Bayesian inference for biophysical neuron models enables stimulus optimization for retinal neuroprosthetics"
Creators
- 1. Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
- 2. Naturwissenschaftliches und Medizinisches Institut an der Universität Tübingen, Reutlingen, Germany
- 3. Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA
Description
Experimental and precomputed data for the paper "Bayesian inference for biophysical neuron models enables stimulus optimization for retinal neuroprosthetics" by Oesterle et al. 2020 (DOI: 10.7554/eLife.54997).
The cone bipolar cell data has been described and published in the paper "Inhibition decorrelates visual feature representations in the inner retina" by Franke et al. 2017 (DOI: 10.1038/nature21394).
This data is both a supplement to the Oesterle et al. paper and the code for this paper.
The code is available in this GitHub repository.
We recommend downloading the GitHub repository and to follow the instructions there.
Files
oesterle_el_al_data.zip
Files
(13.6 GB)
| Name | Size | Download all |
|---|---|---|
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md5:41d116be6781d2dab5ec0a769209743e
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13.6 GB | Preview Download |
Additional details
Related works
- Is supplement to
- Journal article: 10.7554/eLife.54997 (DOI)
- Software: https://github.com/berenslab/CBC_inference (URL)
References
- Oesterle et al. (2020), Bayesian inference for biophysical neuron models enables stimulus optimization for retinal neuroprosthetics, (DOI: 10.7554/eLife.54997)
- Franke et al. (2017), Inhibition decorrelates visual feature representations in the inner retina, (DOI: 10.1038/nature21394)