Federico D’Agostino
@fededagos.bsky.social
PhD student @ Uni Tübingen | @bethgelab.bsky.social | Computational Neuroscience & ML
Thanks for sharing this!
I was not aware of it but looks really relevant. No problem if your lab is no longer working on this much, we will try to incorporate it in the future and reach out if we have any trouble 😉
This is exactly the kind of engagement we hoped to get!
I was not aware of it but looks really relevant. No problem if your lab is no longer working on this much, we will try to incorporate it in the future and reach out if we have any trouble 😉
This is exactly the kind of engagement we hoped to get!
March 17, 2025 at 1:05 PM
Thanks for sharing this!
I was not aware of it but looks really relevant. No problem if your lab is no longer working on this much, we will try to incorporate it in the future and reach out if we have any trouble 😉
This is exactly the kind of engagement we hoped to get!
I was not aware of it but looks really relevant. No problem if your lab is no longer working on this much, we will try to incorporate it in the future and reach out if we have any trouble 😉
This is exactly the kind of engagement we hoped to get!
Try it out and help us improve the accessibility of retinal datasets and models together.
A team effort with:
@thomaszen.bsky.social
@dgonschorek.bsky.social
@lhoefling.bsky.social
@teuler.bsky.social
@bethgelab.bsky.social
#openscience #computationalneuroscience (9/9)
A team effort with:
@thomaszen.bsky.social
@dgonschorek.bsky.social
@lhoefling.bsky.social
@teuler.bsky.social
@bethgelab.bsky.social
#openscience #computationalneuroscience (9/9)
March 14, 2025 at 9:41 AM
Try it out and help us improve the accessibility of retinal datasets and models together.
A team effort with:
@thomaszen.bsky.social
@dgonschorek.bsky.social
@lhoefling.bsky.social
@teuler.bsky.social
@bethgelab.bsky.social
#openscience #computationalneuroscience (9/9)
A team effort with:
@thomaszen.bsky.social
@dgonschorek.bsky.social
@lhoefling.bsky.social
@teuler.bsky.social
@bethgelab.bsky.social
#openscience #computationalneuroscience (9/9)
This is just the beginning.
We see openretina as more than a Python package—it aims to be the start of an initiative to foster open collaboration in computational retina research.
We’d love your feedback! (8/9)
We see openretina as more than a Python package—it aims to be the start of an initiative to foster open collaboration in computational retina research.
We’d love your feedback! (8/9)
March 14, 2025 at 9:41 AM
This is just the beginning.
We see openretina as more than a Python package—it aims to be the start of an initiative to foster open collaboration in computational retina research.
We’d love your feedback! (8/9)
We see openretina as more than a Python package—it aims to be the start of an initiative to foster open collaboration in computational retina research.
We’d love your feedback! (8/9)
Researchers can use openretina to:
✅ Explore pre-trained models in minutes
✅ Train their own models
✅ Contribute datasets & models to the community (7/9)
✅ Explore pre-trained models in minutes
✅ Train their own models
✅ Contribute datasets & models to the community (7/9)
March 14, 2025 at 9:41 AM
Researchers can use openretina to:
✅ Explore pre-trained models in minutes
✅ Train their own models
✅ Contribute datasets & models to the community (7/9)
✅ Explore pre-trained models in minutes
✅ Train their own models
✅ Contribute datasets & models to the community (7/9)
The currently supported models follow a Core + Readout architecture:
🔸 Core: Extracts shared retinal features across data recording sessions
🔸 Readout: Maps shared features to individual neuron responses
🔹 Includes pre-trained models & easy dataset loading (6/9)
🔸 Core: Extracts shared retinal features across data recording sessions
🔸 Readout: Maps shared features to individual neuron responses
🔹 Includes pre-trained models & easy dataset loading (6/9)
March 14, 2025 at 9:41 AM
The currently supported models follow a Core + Readout architecture:
🔸 Core: Extracts shared retinal features across data recording sessions
🔸 Readout: Maps shared features to individual neuron responses
🔹 Includes pre-trained models & easy dataset loading (6/9)
🔸 Core: Extracts shared retinal features across data recording sessions
🔸 Readout: Maps shared features to individual neuron responses
🔹 Includes pre-trained models & easy dataset loading (6/9)
Why does it matter?
Current retina models are often dataset-specific, limiting generalization.
With OpenRetina, we integrate:
🐭 🦎 🐒 Data from multiple species
🎥 Different stimuli & recording modalities
🧠 Deep learning models that can be trained across datasets (5/9)
Current retina models are often dataset-specific, limiting generalization.
With OpenRetina, we integrate:
🐭 🦎 🐒 Data from multiple species
🎥 Different stimuli & recording modalities
🧠 Deep learning models that can be trained across datasets (5/9)
March 14, 2025 at 9:41 AM
Why does it matter?
Current retina models are often dataset-specific, limiting generalization.
With OpenRetina, we integrate:
🐭 🦎 🐒 Data from multiple species
🎥 Different stimuli & recording modalities
🧠 Deep learning models that can be trained across datasets (5/9)
Current retina models are often dataset-specific, limiting generalization.
With OpenRetina, we integrate:
🐭 🦎 🐒 Data from multiple species
🎥 Different stimuli & recording modalities
🧠 Deep learning models that can be trained across datasets (5/9)
What is openretina?
It’s a Python package built on PyTorch, designed for:
🔹 Training deep learning models on retinal data
🔹 Sharing and using pre-trained retinal models
🔹 Cross-dataset, cross-species comparisons
🔹 In-silico hypothesis testing & experiment guidance (4/9)
It’s a Python package built on PyTorch, designed for:
🔹 Training deep learning models on retinal data
🔹 Sharing and using pre-trained retinal models
🔹 Cross-dataset, cross-species comparisons
🔹 In-silico hypothesis testing & experiment guidance (4/9)
March 14, 2025 at 9:41 AM
What is openretina?
It’s a Python package built on PyTorch, designed for:
🔹 Training deep learning models on retinal data
🔹 Sharing and using pre-trained retinal models
🔹 Cross-dataset, cross-species comparisons
🔹 In-silico hypothesis testing & experiment guidance (4/9)
It’s a Python package built on PyTorch, designed for:
🔹 Training deep learning models on retinal data
🔹 Sharing and using pre-trained retinal models
🔹 Cross-dataset, cross-species comparisons
🔹 In-silico hypothesis testing & experiment guidance (4/9)
📄 Paper: www.biorxiv.org/content/10.1...
📦 Code: github.com/open-retina/...
🔧 pip install openretina
📖 Docs: coming soon at open-retina.org (3/9)
📦 Code: github.com/open-retina/...
🔧 pip install openretina
📖 Docs: coming soon at open-retina.org (3/9)
openretina: Collaborative Retina Modelling Across Datasets and Species
Studying the retina plays a crucial role in understanding how the visual world is translated into the brains language. As a stand-alone neural circuit with easily controllable input, the retina provid...
www.biorxiv.org
March 14, 2025 at 9:41 AM
📄 Paper: www.biorxiv.org/content/10.1...
📦 Code: github.com/open-retina/...
🔧 pip install openretina
📖 Docs: coming soon at open-retina.org (3/9)
📦 Code: github.com/open-retina/...
🔧 pip install openretina
📖 Docs: coming soon at open-retina.org (3/9)
Understanding the retina is crucial for decoding how visual information is processed. However, decades of data and models remain scattered across labs and approaches. We introduce openretina to unify retinal system identification. (2/9)
March 14, 2025 at 9:41 AM
Understanding the retina is crucial for decoding how visual information is processed. However, decades of data and models remain scattered across labs and approaches. We introduce openretina to unify retinal system identification. (2/9)