Inhibitory glutamate receptors in spider peripheral mechanosensory neurons
Bibliographic record
Abstract
Most mechanosensory neurons are inhibited by GABAergic efferent neurons. This inhibition is often presynaptic and mediated by ionotropic GABA receptors at the axon terminals. GABA receptor activation opens Cl- channels, leading to membrane depolarization and an increase in membrane conductance. In many invertebrate preparations, efferent neurons that innervate mechanosensory afferents contain glutamate in addition to GABA, suggesting that the sensory neurons are also modulated by glutamate. However, the effects of glutamate on these neurons are not well understood. Peripheral parts of the spider (Cupiennius salei) mechanosensory neurons are surrounded by efferent fibers immunoreactive to antibodies against GABA and glutamate. GABA and its analogue muscimol were shown to effectively inhibit spider mechanosensory neurons innervating lyriform slit sensilla VS-3 that detects cuticular strains in the leg. Here, we show that glutamate also inhibits the VS-3 neurons, but its effects are different from those of GABA or muscimol, suggesting that it acts on a different group of receptors. GABA and muscimol always depolarized these neurons and the inhibitory effect was strongly correlated with the amount of depolarization. In contrast, glutamate inhibited the VS-3 neurons even when it did not depolarize them. In addition, while glutamate inhibited both the axonal action potentials elicited with electrical stimulation and dendritic action potentials produced by mechanical stimulation, muscimol only inhibited the axonal action potentials. Therefore, the inhibitory glutamate receptors in the VS-3 neurons are distinct from and differently distributed than the GABA receptors, providing a subtle control of the neurons' sensitivity in varying behavioural situations.
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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".