Data for Deep-learning models of the ascending proprioceptive pathway are subject to illusions
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.
Cite this item
Perez Rotondo, A., Pigeon, S., David, F., Simos, M., Blanke, O., & Mathis, A. (2025). Data for Deep-learning models of the ascending proprioceptive pathway are subject to illusions (Version 1) [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.15111808
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cfbbe4bd-b9c2-4ccf-af9c-0c7cea80a06c
University of Toronto
EPFL
EPFL
EPFL
EPFL
2025-03-31
1
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 | |
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