Unveiling the Mechanism of Proprioception in Primates: The Application of Task-Driven Neural Network Models
Bibliographic record
Abstract
The paper titled "Task-driven neural network models predict neural dynamics of proprioception" was published in the journal Cell on March 21, 2024, by authors Alessandro Marin Vargas, Axel Bisi, Alberto S. Chiappa, Chris Versteeg, Lee E. Miller, and Alexander Mathis, are from the 1Brain Mind Institute, School of Life Sciences, Ecole Polytechnique Fe´ de´rale de Lausanne (EPFL), 1015 Lausanne, Switzerland; Department of Neuroscience, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA, and others. This study delved into the neural dynamics of proprioception in primates under active and passive movement conditions through the establishment of task-driven neural network models. Utilizing synthetic muscle spindle inputs and musculoskeletal modeling techniques, the research team simulated the proprioceptive process in animals and trained neural networks to solve multiple computational tasks, testing various hypotheses regarding proprioceptive processing. These models were used to predict the neural activity in the cuneate nucleus (CN) and the primary somatosensory cortex (S1) of non-human primates, thereby assessing the effectiveness of various hypotheses in explaining these neural dynamics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".