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Consistent sequential activity across diverse forms of UP states under ketamine anesthesia

2012· article· en· W1940126757 on OpenAlexafffund
Artur Luczak, Péter Barthó

Bibliographic record

VenueEuropean Journal of Neuroscience · 2012
Typearticle
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsUniversity of Lethbridge
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Heritage Foundation for Medical Research
KeywordsNeocortexKetamineLocal field potentialNeuroscienceModulation (music)Premovement neuronal activityBiologyPhysics

Abstract

fetched live from OpenAlex

During slow-wave sleep, the neocortex shows complex, self-organized spontaneous activity. Similar slow-wave oscillations are present under anesthesia where massive, persistent network activity (UP states) alternates with periods of generalized neural silence (DOWN states). To investigate the neuronal activity patterns occurring during UP states, we recorded simultaneously from populations of cells in neocortical layer V of ketamine/xylazine-anesthetized rats. UP states formed a diverse class. In particular, simultaneous-onset UP states were typically accompanied by sharp field potentials and 10-14 Hz modulation, and were often grouped in a 3 Hz ('delta') pattern. Longer, slow-onset UP states did not exhibit 10-14 Hz modulation, and showed a slow propagation across recording electrodes ('traveling waves'). Despite this diversity, the temporal patterns of spiking activity were similar across different UP state types. Analysis of cross-correlograms revealed conserved temporal relationships among neurons, with each neuron having specific timing during UP states. As a group, putative interneurons were most active at the beginning of UP states and putative pyramidal cells were active uniformly throughout the duration of UP states. These results show that UP states under ketamine anesthesia have a stable, fine-structured firing pattern despite a large variability in global structure.

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)0.000
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 score0.457
Threshold uncertainty score0.528

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.078
GPT teacher head0.296
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

Citations47
Published2012
Admission routes2
Has abstractyes

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