AdaptiveMotorControlLab/CEBRA
Learnable latent embeddings for joint behavioral and neural analysis - Official implementation of CEBRA
This project helps neuroscientists and biologists understand the complex relationship between brain activity and observed behavior. By taking recordings of neural data and simultaneous behavioral data, it produces a consistent, high-performance representation of the underlying brain states. Researchers can use this to better decode behavioral variables and analyze how brain activity drives actions.
1,075 stars. Used by 1 other package. Available on PyPI.
Use this if you need to analyze how neural activity correlates with specific behaviors, aiming to find consistent and robust patterns from high-dimensional biological recordings.
Not ideal if your data is not time-series based or if you only have one type of data (e.g., only neural or only behavioral) without auxiliary information for joint analysis.
Stars
1,075
Forks
94
Language
Python
License
—
Category
Last pushed
Feb 02, 2026
Commits (30d)
0
Dependencies
9
Reverse dependents
1
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/AdaptiveMotorControlLab/CEBRA"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
theolepage/sslsv
Toolkit for training and evaluating Self-Supervised Learning (SSL) frameworks for Speaker...
PaddlePaddle/PASSL
PASSL包含 SimCLR，MoCo v1/v2，BYOL，CLIP，PixPro，simsiam, SwAV, BEiT，MAE 等图像自监督算法以及 Vision...
YGZWQZD/LAMDA-SSL
30 Semi-Supervised Learning Algorithms
ModSSC/ModSSC
ModSSC: A Modular Framework for Semi Supervised Classification
microsoft/Semi-supervised-learning
A Unified Semi-Supervised Learning Codebase (NeurIPS'22)