Towards a Reconceptualization of Striatal Interactions Between Glutamatergic and Dopaminergic Neurotransmission and Their Contribution to the Production of Movements
Bibliographic record
Abstract
According to the current model of the basal ganglia organization, simultaneous activation of the striato-nigral direct pathway by glutamatergic and dopaminergic neurotransmission should lead to a synergistic facilitatory action on locomotor activity, while in contrast activation of the indirect pathway by these two neurotransmittions should lead to antagonistic effects on locomotor activity. Based on published data, as a break with the current thinking, we propose a reconceptualization of functional interactions between dopaminergic and glutamatergic neurotransmission. In this model, dopaminergic neurotransmission is seen as a motor pacemaker responsible for the basal and primary activation of striatal output neurons and glutamate as a driver providing a multiple combination of tonic, phasic, facilitatory and inhibitory influxes resulting from the processing of environmental, emotional and mnesic stimuli. Thus, in the model, glutamate-coded inputs would allow tuning the intrinsic motor-activating properties of dopamine to adjust the production of locomotor activity into goal-oriented movements.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".