Metadata-Version: 2.1
Name: openretina
Version: 0.1.0
Summary: Open source retina model architectures and training setups
Author: Federico D'Agostino, Thomas Zenkel, Larissa Höfling
Project-URL: Homepage, https://github.com/open-retina/open-retina
Project-URL: Issues, https://github.com/open-retina/open-retina/issues
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: IPython
Requires-Dist: jaxtyping
Requires-Dist: matplotlib
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: seaborn
Requires-Dist: scipy
Requires-Dist: torch
Requires-Dist: torchaudio
Requires-Dist: torchvision
Requires-Dist: wandb
Requires-Dist: ruff
Requires-Dist: mypy>=1.0
Requires-Dist: pytest
Requires-Dist: types-psutil
Requires-Dist: types-tqdm
Requires-Dist: types-PyYAML

# OpenRetina

Open-source repository containing neural network models of the retina.
The models in this repository are inspired by and partially contain adapted code of [sinzlab/neuralpredictors](https://github.com/sinzlab/neuralpredictors).

## Installation
For normal usage:
```
pip install openretina
```

For development:
```
git clone git@github.com:open-retina/open-retina.git
cd open-retina
pip install -e .
```

## Related papers
The model in `openretina/hoefling_2024` was developed in the paper [A chromatic feature detector in the retina signals visual context changes](https://www.biorxiv.org/content/10.1101/2022.11.30.518492.abstract) and can be cited as:
```
@article{hofling2022chromatic,
  title={A chromatic feature detector in the retina signals visual context changes},
  author={H{\"o}fling, Larissa and Szatko, Klaudia P and Behrens, Christian and Deng, Yuyao and Qiu, Yongrong and Klindt, David A and Jessen, Zachary and Schwartz, Gregory W and Bethge, Matthias and Berens, Philipp and others},
  journal={bioRxiv},
  pages={2022--11},
  year={2022},
  publisher={Cold Spring Harbor Laboratory}
}
```

The paper [Most discriminative stimuli for functional cell type clustering](https://openreview.net/forum?id=9W6KaAcYlr) explains a method to automatically cluster and interpret the modeled neurons and was also used with above model (for code see [ecker-lab/most-discriminative-stimuli](https://github.com/ecker-lab/most-discriminative-stimuli)).

