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Record W2069178880 · doi:10.1121/1.1348004

Neural representation of sound amplitude by functionally different auditory receptors in crickets

2001· article· en· W2069178880 on OpenAlexafffund
Kazuo Imaizumi, Gerald S. Pollack

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2001
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsMcGill University
FundersGovernment of CanadaWhitehall Foundation
KeywordsBiologyCricketNeural codingNeurosciencePopulationLatency (audio)CommunicationAcousticsPsychologyComputer sciencePhysicsEcologyMedicineTelecommunications

Abstract

fetched live from OpenAlex

The physiological characteristics of auditory receptor fibers (ARFs) of crickets, a model system for studying auditory behaviors and their neural mechanisms, are investigated. Unlike auditory receptor neurons of many animals, cricket ARFs fall into three distinct populations based on characteristic frequency (CF) [Imaizumi and Pollack, J. Neurosci. 19, 1508-1516 (1999)]. Two of these have CFs similar to the frequency component of communication signals or of ultrasound produced by predators, and a third population has intermediate CF. Here, sound-amplitude coding by ARFs is examined to gain insights to how behaviorally relevant sounds are encoded by populations of receptor neurons. ARFs involved in acoustic communication comprise two distinct anatomical types, which also differ in physiological parameters (threshold, response slope, dynamic range, minimum latency, and sharpness of tuning). Thus, based on CF and anatomy, ARFs comprise four populations. Physiological parameters are diverse, but within each population they are systematically related to threshold. The details of these relationships differ among the four populations. These findings open the possibility that different ARF populations differ in functional organization.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.933
Threshold uncertainty score0.185

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.025
GPT teacher head0.260
Teacher spread0.234 · 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 designObservational
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

Citations45
Published2001
Admission routes2
Has abstractyes

Explore more

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