%0 Journal Article %1 marinvargas2024taskdriven %A Marin Vargas, Alessandro %A Bisi, Axel %A Chiappa, Alberto S. %A Versteeg, Chris %A Miller, Lee E. %A Mathis, Alexander %B Cell %D 2024 %I Elsevier %J Cell %K biomechanics cuneate_nucleus efference_copy goal-driven_models neural_networks proprioception somatosensory_cortex state_estimation statistics_of_movement task-driven_models %R 10.1016/j.cell.2024.02.036 %T Task-driven neural network models predict neural dynamics of proprioception %U https://doi.org/10.1016/j.cell.2024.02.036 %X Proprioception tells the brain the state of the body based on distributed sensory neurons. Yet, the principles that govern proprioceptive processing are poorly understood. Here, we employ a task-driven modeling approach to investigate the neural code of proprioceptive neurons in cuneate nucleus (CN) and somatosensory cortex area 2 (S1). We simulated muscle spindle signals through musculoskeletal modeling and generated a large-scale movement repertoire to train neural networks based on 16 hypotheses, each representing different computational goals. We found that the emerging, task-optimized internal representations generalize from synthetic data to predict neural dynamics in CN and S1 of primates. Computational tasks that aim to predict the limb position and velocity were the best at predicting the neural activity in both areas. Since task optimization develops representations that better predict neural activity during active than passive movements, we postulate that neural activity in the CN and S1 is top-down modulated during goal-directed movements.

@article{marinvargas2024taskdriven, abstract = {Proprioception tells the brain the state of the body based on distributed sensory neurons. Yet, the principles that govern proprioceptive processing are poorly understood. Here, we employ a task-driven modeling approach to investigate the neural code of proprioceptive neurons in cuneate nucleus (CN) and somatosensory cortex area 2 (S1). We simulated muscle spindle signals through musculoskeletal modeling and generated a large-scale movement repertoire to train neural networks based on 16 hypotheses, each representing different computational goals. We found that the emerging, task-optimized internal representations generalize from synthetic data to predict neural dynamics in CN and S1 of primates. Computational tasks that aim to predict the limb position and velocity were the best at predicting the neural activity in both areas. Since task optimization develops representations that better predict neural activity during active than passive movements, we postulate that neural activity in the CN and S1 is top-down modulated during goal-directed movements.}, added-at = {2024-03-24T10:00:41.000+0100}, author = {Marin Vargas, Alessandro and Bisi, Axel and Chiappa, Alberto S. and Versteeg, Chris and Miller, Lee E. and Mathis, Alexander}, biburl = {https://www.bibsonomy.org/bibtex/2b4210ee8a8ba628ca994b7dd88b967ec/tabularii}, booktitle = {Cell}, comment = {doi: 10.1016/j.cell.2024.02.036}, doi = {10.1016/j.cell.2024.02.036}, interhash = {6e4120c36ee8d5ad0f3626867170422e}, intrahash = {b4210ee8a8ba628ca994b7dd88b967ec}, issn = {00928674}, journal = {Cell}, keywords = {biomechanics cuneate_nucleus efference_copy goal-driven_models neural_networks proprioception somatosensory_cortex state_estimation statistics_of_movement task-driven_models}, publisher = {Elsevier}, timestamp = {2024-03-24T10:00:41.000+0100}, title = {Task-driven neural network models predict neural dynamics of proprioception}, url = {https://doi.org/10.1016/j.cell.2024.02.036}, year = 2024 }