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Contrasting action and posture coding with hierarchical deep neural network models of proprioception, eLife 2023
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Authors: Kai J Sandbrink, Pranav Mamidanna, Claudio Michaelis, Matthias Bethge, Mackenzie W Mathis and Alexander Mathis
Affiliation: Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Switzerland, The Rowland Institute at Harvard, Harvard University, United States; Tübingen AI Center, Eberhard Karls Universität Tübingen & Institute for Theoretical Physics, Germany
Date of upload: December, 2024
Earlier the data was available via dropbox (see github).
Link to the eLife article:
https://elifesciences.org/articles/81499
Here we provide the data and code for this project:
We share the proprioceptive character recognition dataset (contained in 'pcr_data.zip') it has approximately ~29GB when uncompressed.
We share the weights of all the trained networks (contained in 'network-weights.zip'): about ~3.5GB
The compressed code is also available here ('DeepDrawCode.zip').
The activations are shared in a separate Zenodo project (due to the size). Check out the repository below to find the link.
The up to date code is at: https://github.com/amathislab/DeepDraw
The datasets, weights, activations and predictions are released with Creative Commons Attribution 4.0 license.
If you find this useful, please cite:
@article{sandbrink2023contrasting, title={Contrasting action and posture coding with hierarchical deep neural network models of proprioception}, author={Sandbrink, Kai J and Mamidanna, Pranav and Michaelis, Claudio and Bethge, Matthias and Mathis, Mackenzie Weygandt and Mathis, Alexander}, journal={Elife}, volume={12}, pages={e81499}, year={2023}, publisher={eLife Sciences Publications Limited}}
