Activation of GABA<sub>A</sub> receptors modulates all stages of mechanoreception in spider mechanosensory neurons
Bibliographic record
Abstract
GABA(A) receptors mediate mainly inhibitory effects, but there are also many examples of excitatory effects in both mammalian and invertebrate preparations. Here, we aimed to create a complete, quantitative picture of GABA(A)-mediated excitation in a mechanosensory neuron where this phenomenon has been well established. We used muscimol to activate GABA(A) receptors in spider VS-3 neurons and measured the dynamic behavior independently and separately at each of three stages of mechanoreception (receptor current, receptor potential, and action potentials) before and during modulation. We calculated frequency response functions between each stage, estimated information as signal entropy, and estimated information capacity from coherence. Since coherence is sensitive to both noise and nonlinearity, we measured signal-to-noise separately at each stage by averaging responses to repeated mechanical inputs. Muscimol depolarized VS-3 neurons and, after brief inhibition, increased their firing rates. During this excitation, we found significant changes at each stage. Receptor current was attenuated but became more selective to high frequencies. Membrane impedance and time constant fell, favoring higher frequency transmission from receptor current to receptor potential. Action potential firing increased and had higher total entropy. Information capacity from signal-to-noise was always much higher than from coherence, confirming that intracellular noise does not limit signal transmission in these neurons. We conclude that GABA(A) receptor activation shifts each stage of mechanotransduction to higher frequency sensitivity, while the elevated firing rate increases the amount of information that can be encoded. These results show that a single neurotransmitter can finely modulate a sensory neuron's sensitivity and ability to transmit information.
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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) | 0.000 | 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 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".