Random Stimulation of Spider Mechanosensory Neurons Reveals Long-Lasting Excitation by GABA and Muscimol
Bibliographic record
Abstract
gamma-Aminobutyric acid type A (GABA(A)) receptor activation inhibits many primary afferent neurons by depolarization and increased membrane conductance. Deterministic (step and sinusoidal) functions are commonly used as stimuli to test such inhibition. We found that when the VS-3 mechanosensory neurons innervating the spider lyriform slit-sense organ were stimulated by randomly varying white-noise mechanical or electrical signals, their responses to GABA(A) receptor agonists were more complex than the inhibition observed during deterministic stimulation. Instead, there was rapid excitation, then brief inhibition, followed by long-lasting excitation. During the final excitatory phase, VS-3 neuron sensitivity to high-frequency signals increased selectively and their linear information capacity also increased. Using experimental and simulation approaches we found that the excitatory effect could also be achieved by depolarizing the neurons without GABA application and that excitation could override the inhibitory effect produced by increased membrane conductance (shunting). When the VS-3 neurons were exposed to bumetanide, an antagonist of the Cl(-) transporter NKCC1, the GABA-induced depolarization decreased without any change in firing rate, suggesting that the effects of GABA can be maintained for a long time without additional Cl(-) influx. Our results show that the VS-3 neuron's response to GABA depends profoundly on the type of signals the neuron is conveying while the transmitter binds to its receptors.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".