dataset
Data for Deep-learning models of the ascending proprioceptive pathway are subject to illusions
March 31, 2025
Zenodo
Here we provide the data to run the code provided in github.
- Data required to train and test the models in
data.zip
~37 GB when uncompressed. This includes: cleaned_smooth: pre-processed data from FLAG3D and PCR dataset, the elbow flexion datasets used to evaluate the models' performance (EF3D) and effect of vibrations (ES3D). These datasets contain the muscle kinematics used, togther with the spindle models to generate the inputs to the models with spindle inputs. osim: the opensim model used to generate the muscle kinematics for all datasets. spindles: inputs and outputs to the 21 trained models in the paper form the ES3D dataset, used to test tendon vibrations on the models - Data to reproduce the figures in the paper in
data_for_figs.zip
~5.1 GB when uncompressed. - Weights for the 21 trained models analyzed in the paper in
trained_models.zip
~13 MB when uncompressed.
Type
dataset
ACOUA ID
cfbbe4bd-b9c2-4ccf-af9c-0c7cea80a06c
Author(s)
Pigeon, Sebastian
University of Toronto
EPFL
EPFL
EPFL
EPFL
Date Issued
2025-03-31
Version
1
Publisher
License
CC BY
Funder | Funding(s) | Grant NO | Grant URL |
Swiss National Science Foundation | A theory-driven approach to understanding the neural circuits of proprioception | 212516 | |
Relation | Related work | URL/DOI |
IsSupplementTo | Deep-learning models of the ascending proprioceptive pathway are subject to illusions | |
IsSupplementedBy | ||
IsVersionOf | ||
Available on Infoscience
May 27, 2025
Use this identifier to reference this record