Sandbrink, KaiMamidanna, PranavMichaelis, ClaudioBethge, MatthiasMathis, MackenzieMathis, Alexander2025-02-142025-02-142025-02-14202410.5281/zenodo.14544688https://infoscience.epfl.ch/handle/20.500.14299/246910############# Contrasting action and posture coding with hierarchical deep neural network models of proprioception, eLife 2023 ############# 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}}enData for Contrasting action and posture coding with hierarchical deep neural network models of proprioceptiondataset421994ff-152b-4455-82b5-dd4a16465e28