How to cite

Training retinal models using openretina relies on the neural recordings provided by experimental labs, and on the model architectures that we integrated into our package. When using our software for scientific purposes, we encourage you to cite the original data sources that the model you used was trained on, the original papers that developed the model architecture you are using, and, if you are kind, also the repository and paper for our package.

To make this easy, below we provide a starting point with the most important references. Contact us or raise a pull request in case we missed something!

Openretina

• BioRxiv Paper
• Git Repository Citation

Data sources

When using data sources through openretina, we recommend citing both the paper that originally described and analysed the data and the data source location:

• hoefling_2024: Originally published by Höfling et al. (2024), eLife
  • Paper: A chromatic feature detector in the retina signals visual context changes
  • Dataset: originally deposited at https://gin.g-node.org/eulerlab/rgc-natstim
• karamanlis_2024: Originally published by Karamanlis et al. (2024), Nature
  • Paper: Nonlinear receptive fields evoke redundant retinal coding of natural scenes
  • Dataset: Karamanlis D, Gollisch T (2023). Dataset — Marmoset and mouse retinal ganglion cell responses to natural stimuli and supporting data. G-Node. https://doi.org/10.12751/g-node.ejk8kx
• maheswaranathan_2023: Originally published by Maheswaranathan et al. (2023), Neuron
  • Paper: Interpreting the retinal neural code for natural scenes: From computations to neurons
  • Dataset: Maheswaranathan, N., McIntosh, L., Tanaka, H., Grant, S., Kastner, D., Melander, J., Nayebi, A., Brezovec, L., Wang, J., Ganguli, S., Baccus, S. (2023). Interpreting the retinal neural code for natural scenes: from computations to neurons. Stanford Digital Repository. Available at https://purl.stanford.edu/rk663dm5577
• goldin_2022: Originally published by Goldin et al. (2022), Nature Communications
  • Paper: Context-dependent selectivity to natural images in the retina
  • Dataset: originally deposited at https://zenodo.org/records/6868362

Modelling

In addition to the paper mentioned above, here are some influential papers that train artificial neural networks on recordings from visual brain areas like the retina:

• Deep learning models of the retinal response to natural scenes
• Multilayer Recurrent Network Models of Primate Retinal Ganglion Cell Responses
• Neural system identification for large populations separating “what” and “where”

Missing information

If you would like to see additional references on this page, contact us via a GitHub issue or directly raise a pull request for adding the respective information.