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Sound‐guided shaping of the receptive field in the mouse auditory cortex by basal forebrain activation

2005· article· en· W2019626569 on OpenAlexafffund
Jun Yan, Yun Feng Zhang

Bibliographic record

VenueEuropean Journal of Neuroscience · 2005
Typearticle
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsUniversity of Calgary
FundersFondation pour la Recherche MédicaleInstitute of Neurosciences, Mental Health and AddictionFondation Pour l'Audition
KeywordsBasal forebrainReceptive fieldAuditory cortexNeuroscienceForebrainSound (geography)PsychologyBasal (medicine)BiologyAcousticsPhysicsCentral nervous systemEndocrinology

Abstract

fetched live from OpenAlex

The mammalian auditory cortex undergoes continuous plasticity following auditory experience. This study demonstrates the instructive roles of sound frequency and amplitude in representational plasticity in the primary auditory cortex of the mouse. Electrical stimulation of the basal forebrain paired with a tone led to a pronounced shift in the receptive field of the cortical neurons in both frequency and amplitude domains, the shift being towards the frequency and amplitude of the tone. Importantly, the plasticity in the frequency tuning of cortical neurons appeared to be largely dependent upon frequency-specific decreases in the response threshold. The minimum threshold of cortical neurons could be reduced only if the amplitude of the presented tone was lower than the minimum threshold. This finding suggests that training with low-intensity sound can increase the sensitivity of cortical neurons. Furthermore, all of these effects evoked by basal forebrain activation could be eliminated by cortical application of atropine, the acetylcholine muscarinic receptor antagonist. The data suggest that cortical plasticity is guided by both sound frequency and amplitude. The basal forebrain promotes sound-guided cortical plasticity by facilitating neural mechanisms intrinsic to the auditory system.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.223
Threshold uncertainty score0.308

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 science0.0010.000
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 head0.052
GPT teacher head0.270
Teacher spread0.218 · 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

Citations57
Published2005
Admission routes2
Has abstractyes

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