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Record W2136985327 · doi:10.1152/jn.00111.2004

Differential Temporal Coding of Rhythmically Diverse Acoustic Signals by a Single Interneuron

2004· article· en· W2136985327 on OpenAlexaff
Gary Marsat, Gerald S. Pollack

Bibliographic record

VenueJournal of Neurophysiology · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBat Biology and Ecology Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsBinaural recordingHuman echolocationAcousticsNeural codingFrequency modulationNeuroscienceInterneuronCommunicationCoding (social sciences)Amplitude modulationPhysicsSpeech recognitionPsychologyComputer scienceMathematicsInhibitory postsynaptic potentialRadio frequencyTelecommunications

Abstract

fetched live from OpenAlex

The omega neuron 1 (ON1) of the cricket Teleogryllus oceanicus responds to conspecific signals (4.5 kHz) and to the ultrasonic echolocation sounds used by hunting, insectivorous bats. These signals differ in temporal structure as well as in carrier frequency. We show that ON1's temporal coding properties vary with carrier frequency, allowing it to encode both of these behaviorally important signals. Information-transfer functions show that coding of 4.5 kHz is limited to the range of amplitude-modulation components that occur in cricket songs (<32 Hz), whereas coding of 30-kHz stimuli extends to the higher pulse rates that occur in bat sounds ( approximately 100 Hz). Nonlinear coding contributes to the information content of ON1's spike train, particularly for 30-kHz stimuli with high intensities and large modulation depths. Phase locking to sinusoidal amplitude envelopes also extends to higher AM frequencies for ultrasound stimuli. ON1s frequency-specific behavior cannot be ascribed to differences in the shapes of information-transfer functions of low- and high-frequency-tuned receptor neurons, both of which are tuned more broadly to AM frequencies than ON1. Coding properties are nearly unaffected by contralateral deafferentation. ON1's role in auditory processing is to increase binaural contrast through contralateral inhibition. We hypothesize that its frequency-specific temporal coding properties optimize binaural contrast for sounds with both the spectral and temporal features of behaviorally relevant signals.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.782
Threshold uncertainty score0.201

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 head0.222
Teacher spread0.197 · 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

Citations54
Published2004
Admission routes1
Has abstractyes

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