Title
WhoBPyT - Whole-Brain Modelling in PyTorch
Short description and the goals for the OHBM BrainHack
WhoBPyT is a Python library for connectome-based neurophysiological modelling of neuroimaging and other macroscale brain data. At its core is a deep learning-based approach for model fitting and parameter estimation, that capitalizes on the in-built algorithmic differentiation and optimization functionality of PyTorch.
The current WhoBPyT code base is a consolidation of several projects that have used this computational technique to study mechanisms underlying brain dynamics in TMS-EEG, stereotactic EEG, resting-state fMRI.
We have completed a major refactor of the code base that generalizes several key functionalities and improves overall readability. The goal of this OHBM BrainHack project is to improve the first-time user experience with WhoBPyT, principally through docs and tutorial examples on computational modeling of EEG and fMRI data.
Link to the Project
https://github.com/GriffithsLab/whobpyt
Image/Logo for the OHBM brainhack website
https://drive.google.com/uc?export=view&id=1wJaFEEk6hDfeOCjix2v_BhmWmNak9VzO
Project lead
John Griffiths
Main Hub
Seoul
Link to the Project pitch
Pitch video will be added ahead of the event.
Other hubs covered by the leaders
Skills
Python
PyTorch
Github
EEG
fMRI
MNE
Nilearn
Numerical simulation
Reading+writing
Recommended tutorials for new contributors
OHBM brain modelling edu course - https://griffithslab.github.io/OHBM2024-educational-course/
Good first issues
- Test the EEG/fMRI modelling examples
- Run examples with alternative data
- Improve data visualization in examples
- Add any missing docstrings
- Add missing items to the README
- ...
Twitter summary
No response
Short name for the Discord chat channel (~15 chars)
PyTorchBrainModelling
Please read and follow the OHBM Code of Conduct
Title
WhoBPyT - Whole-Brain Modelling in PyTorch
Short description and the goals for the OHBM BrainHack
WhoBPyT is a Python library for connectome-based neurophysiological modelling of neuroimaging and other macroscale brain data. At its core is a deep learning-based approach for model fitting and parameter estimation, that capitalizes on the in-built algorithmic differentiation and optimization functionality of PyTorch.
The current
WhoBPyTcode base is a consolidation of several projects that have used this computational technique to study mechanisms underlying brain dynamics in TMS-EEG, stereotactic EEG, resting-state fMRI.We have completed a major refactor of the code base that generalizes several key functionalities and improves overall readability. The goal of this OHBM BrainHack project is to improve the first-time user experience with
WhoBPyT, principally through docs and tutorial examples on computational modeling of EEG and fMRI data.Link to the Project
https://github.com/GriffithsLab/whobpyt
Image/Logo for the OHBM brainhack website
https://drive.google.com/uc?export=view&id=1wJaFEEk6hDfeOCjix2v_BhmWmNak9VzO
Project lead
John Griffiths
Main Hub
Seoul
Link to the Project pitch
Pitch video will be added ahead of the event.
Other hubs covered by the leaders
Skills
Python
PyTorch
Github
EEG
fMRI
MNE
Nilearn
Numerical simulation
Reading+writing
Recommended tutorials for new contributors
OHBM brain modelling edu course - https://griffithslab.github.io/OHBM2024-educational-course/
Good first issues
Twitter summary
No response
Short name for the Discord chat channel (~15 chars)
PyTorchBrainModelling
Please read and follow the OHBM Code of Conduct