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Reliable Sex and Strain Discrimination in the Mouse Vomeronasal Organ and Accessory Olfactory Bulb

2013· article· en· W2158981585 on OpenAlexfundno aff
Illya I. Tolokh, Xiaoyan Fu, Timothy E. Holy

Bibliographic record

VenueJournal of Neuroscience · 2013
Typearticle
Languageen
FieldNeuroscience
TopicOlfactory and Sensory Function Studies
Canadian institutionsnot available
FundersNational Institute of Neurological Disorders and StrokeNational Institute on Deafness and Other Communication DisordersNatural Sciences and Engineering Research Council of CanadaNational Institutes of HealthG. Harold and Leila Y. Mathers Foundation
KeywordsVomeronasal organBiologyStimulus (psychology)NeuroscienceOlfactionOlfactory systemPopulationOdorOlfactory bulbNeutral stimulusNeural codingPheromonePsychologyStimulus controlCentral nervous systemGenetics

Abstract

fetched live from OpenAlex

Animals modulate their courtship and territorial behaviors in response to olfactory cues produced by other animals. In rodents, detecting these cues is the primary role of the accessory olfactory system (AOS). We sought to systematically investigate the natural stimulus coding logic and robustness in neurons of the first two stages of accessory olfactory processing, the vomeronasal organ (VNO) and accessory olfactory bulb (AOB). We show that firing rate responses of just a few well-chosen mouse VNO or AOB neurons can be used to reliably encode both sex and strain of other mice from cues contained in urine. Additionally, we show that this population code can generalize to new concentrations of stimuli and appears to represent stimulus identity in terms of diverging paths in coding space. Together, the results indicate that firing rate code on the temporal order of seconds is sufficient for accurate classification of pheromonal patterns at different concentrations and may be used by AOS neural circuitry to discriminate among naturally occurring urine stimuli.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.947
Threshold uncertainty score0.272

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.154
GPT teacher head0.285
Teacher spread0.130 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations51
Published2013
Admission routes1
Has abstractyes

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