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Record W4410218966 · doi:10.1016/j.celrep.2025.115678

Reliable sensory processing of superficial cortical interneurons is modulated by behavioral state

2025· article· en· W4410218966 on OpenAlexfundno aff
Carolyn G. Sweeney, Maryse E. Thomas, Christine Liu, Lucas G. Vattino, Kasey E Smith, Anne E. Takesian

Bibliographic record

VenueCell Reports · 2025
Typearticle
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsnot available
FundersNational Institute on Deafness and Other Communication DisordersFonds de recherche du Québec – Nature et technologiesFondation BertarelliNatural Sciences and Engineering Research Council of CanadaNancy Lurie Marks Family Foundation
KeywordsNeuroscienceSensory processingSensory systemCortical neuronsBiology

Abstract

fetched live from OpenAlex

GABAergic interneurons in cortical layer 1 (L1) integrate sensory and top-down inputs to modulate network activity and support learning-related plasticity. However, little is known about how sensory inputs drive L1 interneuron activity. We used two-photon calcium imaging to measure sound-evoked responses in two L1 interneuron populations expressing vasoactive intestinal peptide (VIP) or neuron-derived neurotrophic factor (NDNF) in mouse auditory cortex. We found that L1 interneurons respond to both simple and complex sounds, but their responses are highly variable across trials. Despite this variability, these interneurons respond reliably to a narrow range of stimuli, reflecting selectivity for specific spectrotemporal sound features. Response reliability was modulated by behavioral state and predicted by the activity of neighboring interneurons. These findings reveal that L1 interneurons exhibit sensory tuning and identify the modulation of response reliability as a potential mechanism by which L1 relays state-dependent cues to shape sensory representations.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.274
Teacher spread0.253 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2025
Admission routes1
Has abstractyes

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