MAPPING OF THE SPINAL CIRCUITRY ASSOCIATED WITH PAW WITHDRAWAL LEARNING IN SPINAL MICE
Bibliographic record
Abstract
MAPPING OF THE SPINAL CIRCUITRY ASSOCIATED WITH PAW WITHDRAWAL LEARNING IN SPINAL MICE Raefsky, S.M., Joseph, S.M., Xiao, M.S., Hornak, A.J., Kim, J.A., Tillakaratne, N.J.K. and Edgerton, V.R. Affiliation: 1University of Puget Sound, Tacoma, WA, 1University of California Los Angeles, Los Angeles, CA PURPOSE: The overall goal of this project was to identify the neural circuitry involved in paw withdrawal learning (PaWL) in complete spinal cord transected (ST) mice. METHODS: Pseudo rabies virus (PRV)-Bartha 152 was injected into the tibialis anterior (TA), the primary muscle involved in this learning. The use of PRV, a trans synaptic retrograde marker, allowed labeling of the TA Moto neurons and its associated interneurons in the spinal cord. By combining PRV-Bartha 152 with c-fos (an activity-dependent marker) and CaMKII (a learning-associated marker), the activated Moto neurons and interneurons that were associated with spinal learning were identified. RESULTS: Of all PRV+ labeled neurons, 21% were Moto neurons and found only on the ipsilateral side of the spinal cord (same side where learning occurred). Sixty-five percent of the labeled interneurons were found on the ipsilateral side of the spinal cord and 14% were found on the contralateral side. A majority (~60%) of the interneurons and Moto neurons on the ipsilateral side were activated during PaWL. CONCLUSION: Overall, activated PRV+ interneurons that were also positive for CaMKII were mostly located in laminae VI-VII, suggesting that the neural circuitry involved in PaWL occurred in these regions. Supported by the Neilsen Foundation (JAK), Christopher and Dana Reeve Foundation (VRE), Kirby Foundation (VRE), Walk About Foundation (VRE), and a University of Puget Sound 2013 Summer Research Grant.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".