Single olfactory sensory neurons simultaneously integrate the components of an odour mixture
Bibliographic record
Abstract
Most odours are complex mixtures. However, the capacities of olfactory sensory neurons (OSNs) to process complex odour stimuli have never been explored in air-breathing vertebrates. To face this issue, the present study compares the electrical responses of single OSNs to two odour molecules, delivered singly and mixed together, in rats in vivo. This work is the first aimed at demonstrating that single OSNs simultaneously integrate several chemical signals and which, furthermore, attempts to describe such processes for the whole concentration range over which single OSNs can work. The results stress that complex interactions occur between components in odour mixtures and that OSN responses to such mixtures are not simply predictable from the responses to their components. Three types of interactions are described. They are termed suppression, hypoadditivity and synergy, in accord with psychophysical terminology. This allows us to draw links between peripheral odour reception and central odour coding. Indeed, events occurring in single OSNs may account for the dominating or even the masking effects of odour molecules in complex mixtures, i.e. for the prevailing action of a minor component in the final qualitative perception of a mixture. We conclude that our observations with binary mixtures anticipate the complexity of processes which may rise at the level of a single OSN in physiological conditions. Following this hypothesis, a natural odour would induce a multi-chemical integration at the level of single OSNs which may result in refining their individual odour-coding properties, leading them to play a crucial role in the final performance of the olfactory system.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".