Published January 18, 2024 | Version v1

Task-driven neural network models predict neural dynamics of proprioception: Synthetic muscle spindle datasets

Description

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Task-driven neural network models predict neural dynamics of proprioception, Cell 2024

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Authors: Marin Vargas, Alessandro (orcid=0000-0001-7073-4120) and Bisi, Axel (orcid=0009-0006-8602-7555) and Chiappa, Alberto Silvio (orcid=0009-0001-2764-6552) and Versteeg, Christopher (orcid=0000-0002-4269-5109) and Miller, Lee E. (orcid=0000-0001-8675-7140) and Mathis, Alexander (orcid=0000-0002-3777-2202)

Affiliation: EPFL

Date: January, 2024

Link to the Cell article: 

https://www.cell.com/cell/pdf/S0092-8674(24)00239-3.pdf

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Here we provide the synthetic spindle datasets of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the synthetic generated training dataset of simulated muscle spindles during arm passive movements generated with either character writing (PCR) or with 3D target reaching using reinforcement learning (RL).

The overall structure of the data is:

└── spindle_datasets
    ├── pcr_dataset                     - Contains PCR synthetic training dataset
    └── rl_dataset                        - Contains RL-generated synthetic training dataset

The code to generate the PCR synthetic spindle dataset is available at: https://github.com/amathislab/Task-driven-Proprioception/tree/master/PCR-data-generation

The code to generate the RL-generated synthetic spindle dataset is available at: https://github.com/amathislab/Task-driven-Proprioception/tree/master/RL-data-generation

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The datasets, weights, activations and predictions are released with Creative Commons Attribution 4.0 license.

The code is released under the MIT license, see https://github.com/amathislab/Task-driven-Proprioception

If you find our code, weights, predictions or ideas useful, please cite:

@article{vargas2024task,
  title={Task-driven neural network models predict neural dynamics of proprioception},
  author={{Marin Vargas}, Alessandro and Bisi, Axel and Chiappa, Alberto S and Versteeg, Chris and Miller, Lee E and Mathis, Alexander},
  journal={Cell},
  year={2024},
  publisher={Elsevier}
}

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Additional details

Funding

Swiss National Science Foundation
A theory-driven approach to understanding the neural circuits of proprioception 212516