The Bidirectionality of Motor Learning in the Vestibulo-ocular Reflex Is a Function of Cerebellar mGluR1 Receptors
Bibliographic record
Abstract
Bidirectional changes in synaptic transmission have the potential to optimize the control of movement. However, it can be difficult to establish a causal relationship between the bidirectionality of synaptic plasticity and bidirectional changes in the speed of actual movements. We asked whether metabotropic glutamate receptor 1 (mGluR1) receptors, which participate in cerebellar long-term depression (LTD), are necessary for bidirectional motor learning in the vestibulo-ocular reflex (VOR). Cerebellar LTD and long-term potentiation (LTP) are thought to cause increases and decreases, respectively, in the gain of the VOR; the direction of learning depends on the behavioral protocol. We injected either the mGluR1 agonist (S)-DHPG or the antagonist YM 298198 bilaterally into the flocculus of alert cats, and then induced motor learning. In the presence of YM 298198, the VOR gain decreased in gain-up, as well as in gain-down protocols. (S)-DHPG augmented gain-up learning. Gain-down learning was not significantly affected by either drug. These results supported the hypothesis that gain-up learning relies on cerebellar LTD, but gain-down learning relies on a different mechanism. In the absence of mGluR1 activity, cerebellar LTD may be replaced with LTP, permitting learning in only one direction.
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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 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 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".