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Record W2169426755 · doi:10.1186/1744-8069-8-53

Characterization of Neuronal Intrinsic Properties and Synaptic Transmission in Layer I of Anterior Cingulate Cortex from Adult Mice

2012· article· en· W2169426755 on OpenAlexafffund
Xiang‐Yao Li, Tao Chen, Giannina Descalzi, Kohei Koga, Shuang Qiu, Min Zhuo

Bibliographic record

VenueMolecular Pain · 2012
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsCanada Research ChairsUniversity of TorontoUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsAnterior cingulate cortexNeuroscienceNeurotransmissionCingulate cortexPsychologyMedicineReceptorCentral nervous systemCognitionInternal medicine

Abstract

fetched live from OpenAlex

The neurons in neocortex layer I (LI) provide inhibition to the cortical networks. Despite increasing use of mice for the study of brain functions, few studies were reported about mouse LI neurons. In the present study, we characterized intrinsic properties of LI neurons of the anterior cingulate cortex (ACC), a key cortical area for sensory and cognitive functions, by using whole-cell patch clamp recording approach. Seventy one neurons in LI and 12 pyramidal neurons in LII/III were recorded. Although all of the LI neurons expressed continuous adapting firing characteristics, the unsupervised clustering results revealed five groups in the ACC, including: Spontaneous firing neurons; Delay-sAHP neurons, Delay-fAHP neurons, and two groups of neurons with ADP, named ADP1 and ADP2, respectively. Using pharmacological approaches, we found that LI neurons received both excitatory (mediated by AMPA, kainate and NMDA receptors), and inhibitory inputs (which were mediated by GABA(A) receptors). Our studies provide the first report characterizing the electrophysiological properties of neurons in LI of the ACC from adult mice.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.377

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 head0.029
GPT teacher head0.267
Teacher spread0.239 · 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

Citations8
Published2012
Admission routes2
Has abstractyes

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