🦓CEBRA
@cebraAI
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🦓 Consistent EmBeddings of high-dimensional Recordings using Auxiliary variables | pip install cebra
Dev by @mwmathislab
Joined September 2021
We have a new release up! Check out 0.5.0rc1!! 🔥 Important maintenance updates to keep up with dependencies, some nee features, and preparing for much more coming in the next weeks 👀… #cebra @mwmathislab
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RT @cebraAI: Exciting! There is a new demo notebook for how to use #CEBRA on @AllenInstitute OpenScope project data! 🎉 contributed by @Leco…
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RT @yonicoud: Population dynamics and deep-learning analyses (@cebraAI @TrackingActions) of anterior insula single-unit recordings uncover…
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🥳 check out the #DeepLabCut + #CEBRA work for behavioral analysis from @shaokaiyeah et al @NatureComms ⬇️
#SfN24 update: sadly, we are not there in person, but we love to see a lot of #DeepLabCut powered science is 🥳 and we are happy to retweet it! 💕 (tag us!) 👀 But, here is what we would present 🥰🔥 DeepLabCut 3.0 release candidate is up!🔥 pip install deeplabcut==3.0.0rc5
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RT @JanGrundemann: Meet @hziyan and @JensBlack3 at our #SfN24 poster on imaging neuronal population dynamics during memory consolidation an…
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I have been reviewing the CEBRA paper in depth. What really strikes me is the multi-sessions/animal embedding. I feel like this paper is only scratching the surface of what could be done there. For starter, we could embed across species or even between models and experiments.
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RT @TrackingActions: Delighted to play a small part in this tour de force from @Simon_CEChang et al ! Beautiful merger of many behavioral…
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RT @neurodaniela: Mackenzie Mathis @TrackingActions @EPFL_en started #Cajal2024 with an intro to the study of movement. After, she presente…
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@AheadOfTheNerve @WeissShahaf I wouldn’t recommend different iterations and temperatures if you want to head-to-head compare model infoNCE loss values; consistency would be okay though, as if you want to be sure they are each trained to be across-run consistent, then fine across animals/models
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RT @TrackingActions: 🦓 Self-supervised multimodal ML is promising the next AI breakthrough - in our new work published in @Nature, we debut…
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#CEBRA🦓can be used to build a unified neural encoding model using data from many animals, and that can then be deployed on new animals for BMI-style decoding 🦾
🔥what was super exciting is the pre-trained encoder can be used to rapidly adapt to a new dataset, which doesn’t have the same # of neurons, etc, and by rapid, in this case less than 1 sec of new data! (And this is with a super simple MLP NN and kNN decoder!)
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What a year! ✨🎊🦓 In 2023 #CEBRA was released 🎊 We are humbled & proud that it’s being used by many: 14K pip installs and 75 citations in 8 months 🙏🏼🚀 Thank you all ❤️— you make all the coding & work worth it! 💪 Onwards to 2024, with even more on the way 🦓🔥✨
🦓 Self-supervised multimodal ML is promising the next AI breakthrough - in our new work published in @Nature, we debut @CEBRAai: for self-supervised hypothesis- and discovery-driven science. 📝 💻 🦓 🧵⬇️
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RT @neumann_wj: Open Science in action: The incredibly talented @MerkTimon from our @ICNeuromodulate team has made his first contribution t…
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