Abstracts for 2014th Annual Poster Presentation (Research Institute for Diseases of Old Age)
Bibliographic record
Abstract
Background: In-vivo voltammetry has successfully been used to detect dopamine release in rodent brains, but its application to monkeys has been limited.We have previously detected dopamine release in the caudate of behaving Japanese monkeys using diamond microelectrodes (Yoshimi 2011); however it is not known whether the release pattern is the same in various areas of the forebrain.Recent studies have suggested variations in the dopaminergic projections to forebrain areas.Methods: In the present study, we attempted simultaneous recording at two locations in the striatum.Carbon-fiber microelectrodes were inserted into the striatum of two Japanese monkeys.Dopamine release was detected by fast-scan cyclic voltammetry (FSCV) on the carbon fibers, which has been widely used in rodents.Results: Dopamine responses to unpredicted food and liquid rewards were detected repeatedly.The response to the liquid reward after conditioned stimuli was enhanced after switching the prediction cue.Conclusion: These results are consistent to the "reward-prediction error"theory of dopamine release.The characteristics were generally similar between the ventral striatum and the putamen.Overall, the technical application of FSCV recording in multiple locations was successful in behaving primates, and further voltammetric recordings in multiple locations will expand our knowledge of dopamine reward responses.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.523 | 0.301 |
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".