Dynamic Characterization of<i>Drosophila</i>Antennal Olfactory Neurons Indicates Multiple Opponent Signaling Pathways in Odor Discrimination
Bibliographic record
Abstract
Opponent signaling refers to processes in which antagonistic signals are produced by different, but closely related, stimuli. It allows enhanced discrimination and more accurate behavioral responses. We explored opponent signaling in the Drosophila melanogaster olfactory system by measuring frequency response functions between odorant concentrations and primary olfactory neuron responses. Random fluctuations in concentration of two aliphatic and two aromatic fruit odorants were used to modulate action potentials from basiconic antennal sensilla. We separated action potentials by two-dimensional cluster analysis using amplitude and cross-correlation with a median action-potential template. Frequency response functions were fitted with either bandpass or second-order low-pass functions and then divided into two polarity groups, excitatory and inhibitory, by fitting the frequency response functions. Cluster analysis gave two, three, or four action potential clusters for each sensillum recording. Sensilla were then grouped by the patterns of response polarities of the individual neurons into four sensillum types, one with four neurons and three with two neurons. All four odorant compounds produced a mixture of excitatory, inhibitory, and null responses in different neurons. Statistical analysis of frequency response parameters for individual odorants gave only weak correlation between dynamics and some neuron types, even when comparing the dynamics of excitatory and inhibitory responses to the same odorant. However, response dynamics were significantly different between aliphatic and aromatic compounds, and between the two aliphatic compounds. Each odorant caused opposing excitatory and inhibitory signals to be sent to the antennal lobe along at least two pairs of axonal pathways.
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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".