Virtual Reality Platform for Systematic Investigation of Multisensory Integration and Training of Closed-Loop Prosthetic Control
Bibliographic record
Abstract
Multisensory integration is the process by which information from different sensory modalities is integrated by the nervous system. Understanding this process is important not only from a basic science perspective but also for translational reasons, e.g. for the development of closed-loop neural prosthetic systems. Here we describe a versatile virtual reality platform which can be used to study the neural mechanisms of multisensory integration for the upper limb and could potentially be incorporated into systems for training of robust neural prosthetic control. The platform involves the interaction of multiple computers and programs and allows for selection of different avatar arms and for modification of a selected arm's visual properties. The system was tested with two non-human primates (NHP) that were trained to reach to multiple targets on a tabletop. Reliability of arm visual feedback was altered by applying different levels of blurring to the arm. In addition, tactile feedback was altered by adding or removing physical targets from the environment. We observed differences in movement endpoint distributions that varied between animals and visual feedback conditions, as well as across targets. The results indicate that the system can be used to study multisensory integration in a well-controlled manner.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".