Acquisition of non-olfactory encoding improves odour discrimination in olfactory cortex
Bibliographic record
Abstract
Abstract Primary sensory cortices, initially considered elementary encoders of physicochemical attributes of environmental stimuli, are now known to be modulated by other aspects of experience, such as attentional state and internal expectations 1–3 , movement-related signals 4–7 and spatial information 2, 8, 9 . However, the specific role of these signals in cortical sensory processing is not fully understood 10 . Here we reveal multiple and diverse non-olfactory responses in the primary olfactory (piriform) cortex (PCx), which dynamically enhance PCx odour discrimination according to behavioural demands. We designed a behavioural task using a virtual reality environment and performed recordings in PCx neurons. In this task, mice were trained to associate specific odours with visual contexts in order to receive a reward. We found that learning shifts PCx activity from encoding solely odour identity to a more complex regime. In this regime, positional, contextual, and associative responses emerge on odour-responsive neurons that thus become mixed-selective. Contextual information is sustained in PCx activity of expert animals, specifically when visual context identity is needed to solve the task. After learning, odours are better decoded from PCx activity when mice are engaged in the task and when odours are presented within a rewarded context. This enhancement of PCx olfactory processing is reliant on the acquired mixed-selectivity. Thus, the integration of extra-sensory inputs within primary sensory cortices can encode the behavioural relevance of encountered stimuli while improving sensory processing.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".