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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}}