The Long and Winding Road to Gamma-Amino-Butyric Acid as Neurotransmitter
Bibliographic record
Abstract
Abstract This review centers on the discoveries made during more than six decades of neuroscience research on the role of gamma-amino-butyric acid (GABA) as neurotransmitter. In doing so, special emphasis is directed to the significant involvement of Canadian scientists in these advances. Starting with the early studies that established GABA as an inhibitory neurotransmitter at central synapses, we summarize the results pointing at the GABA receptor as a drug target as well as more recent evidence showing that GABAAreceptor signaling plays a surprisingly active role in neuronal network synchronization, both during development and in the adult brain. Finally, we briefly address the involvement of GABA in neurological conditions that encompass epileptic disorders and mental retardation. RESUMÉ: Le chemin long et sinueux pour que le GABA soit reconnu comme un neurotransmetteur.Cette revue est axée sur les découvertes réalisées durant plus de six décennies de recherche en neurosciences sur l’acide gamma-aminobutyrique (GABA) comme neurotransmetteur. À cet effet, nous mettons une emphase particulière sur le rôle significatif de chercheurs canadiens dans ce domaine de recherche. En prenant comme point de départ les premières études qui ont établi que le GABA était un neurotransmetteur au niveau de synapses centrales, nous faisons le sommaire des résultats identifiant le récepteur GABA comme étant une cible thérapeutique ainsi que des données plus récentes montrant que la signalisation du récepteur GABAA joue, de façon surprenante, un rôle actif dans la synchronisation du réseau neuronal, tant au cours du développement que dans le cerveau adulte. Finalement, nous traitons brièvement du rôle de GABA dans les maladies neurologiques incluant les troubles épileptiques et l’arriération mentale.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".