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Record W2548290184 · doi:10.1111/ejn.13463

Ageing affects dual encoding of periodicity and envelope shape in rat inferior colliculus neurons

2016· article· en· W2548290184 on OpenAlexaff
Björn Herrmann, Aravindakshan Parthasarathy, Edward L. Bartlett

Bibliographic record

VenueEuropean Journal of Neuroscience · 2016
Typearticle
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsWestern University
FundersNational Institute on Deafness and Other Communication Disorders
KeywordsInferior colliculusContext (archaeology)NeuroscienceEnvelope (radar)Modulation (music)Local field potentialAgeingPhysicsSpike (software development)BiologyAcousticsComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

Extracting temporal periodicities and envelope shapes of sounds is important for listening within complex auditory scenes but declines behaviorally with age. Here, we recorded local field potentials (LFPs) and spikes to investigate how ageing affects the neural representations of different modulation rates and envelope shapes in the inferior colliculus of rats. We specifically aimed to explore the input-output (LFP-spike) response transformations of inferior colliculus neurons. Our results show that envelope shapes up to 256-Hz modulation rates are represented in the neural synchronisation phase lags in younger and older animals. Critically, ageing was associated with (i) an enhanced gain in onset response magnitude from LFPs to spikes; (ii) an enhanced gain in neural synchronisation strength from LFPs to spikes for a low modulation rate (45 Hz); (iii) a decrease in LFP synchronisation strength for higher modulation rates (128 and 256 Hz) and (iv) changes in neural synchronisation strength to different envelope shapes. The current age-related changes are discussed in the context of an altered excitation-inhibition balance accompanying ageing.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.732
Threshold uncertainty score0.466

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.034
GPT teacher head0.247
Teacher spread0.214 · 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

Citations49
Published2016
Admission routes1
Has abstractyes

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