Discovering the Intriguing Properties of Extrasynaptic γ-Aminobutyric Acid Type A Receptors
Bibliographic record
Abstract
Tonic inhibition in mouse hippocampal CA1 pyramidal neurons is mediated by α5 subunit-containing γ-aminobutyric acid type A receptors. By Caraiscos VB, Elliott EM, You-Ten KE, Cheng VY, Belelli D, Newell JG, Jackson MF, Lambert JJ, Rosahl TW, Wafford KA, MacDonald JF, Orser BA. Proc Natl Acad Sci U S A 2004; 101:3662-7. Reprinted with permission. In this Classic Paper Revisited, the author recounts the scientific journey leading to a report published in the Proceedings of the National Academy of Sciences (PNAS) and shares several personal stories from her formative years and "research truths" that she has learned along the way. Briefly, the principal inhibitory neurotransmitter in the brain, γ-aminobutyric acid (GABA), was conventionally thought to regulate cognitive processes by activating synaptic GABA type A (GABAA) receptors and generating transient inhibitory synaptic currents. However, the author's laboratory team discovered a novel nonsynaptic form of tonic inhibition in hippocampal pyramidal neurons, mediated by extrasynaptic GABAA receptors that are pharmacologically distinct from synaptic GABAA receptors. This tonic current is highly sensitive to most general anesthetics, including sevoflurane and propofol, and likely contributes to the memory-blocking properties of these drugs. Before the publication in PNAS, the subunit composition of GABAA receptors that generate the tonic current was unknown. The team's research showed that GABAA receptors containing the α5 subunit (α5GABAARs) generated the tonic inhibitory current in hippocampal neurons. α5GABAARs are highly sensitive to GABA, desensitize slowly, and are thus well suited for detecting low, persistent, ambient concentrations of GABA in the extracellular space. Interest in α5GABAARs has surged since the PNAS report, driven by their pivotal roles in cognitive processes and their potential as therapeutic targets for treating various neurologic disorders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".