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Record W2315504035 · doi:10.3791/53576

Induction of an Isoelectric Brain State to Investigate the Impact of Endogenous Synaptic Activity on Neuronal Excitability <em>In Vivo</em>

2016· article· en· W2315504035 on OpenAlexaff
Tristan Altwegg‐Boussac, Séverine Mahon, Mario Chávez, Stéphane Charpier, Adrien E. Schramm

Bibliographic record

VenueJournal of Visualized Experiments · 2016
Typearticle
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsCanadian Nautical Research Society
FundersFondation de FranceInstitut National de la Santé et de la Recherche MédicaleAgence Nationale de la Recherche
KeywordsNeuroscienceElectrophysiologyIn vivoPremovement neuronal activityNeuronIntracellularBiologyChemistryCell biology

Abstract

fetched live from OpenAlex

The way neurons process information depends both on their intrinsic membrane properties and on the dynamics of the afferent synaptic network. In particular, endogenously-generated network activity, which strongly varies as a function of the state of vigilance, significantly modulates neuronal computation. To investigate how different spontaneous cerebral dynamics impact single neurons' integrative properties, we developed a new experimental strategy in the rat consisting in suppressing in vivo all cerebral activity by means of a systemic injection of a high dose of sodium pentobarbital. Cortical activities, continuously monitored by combined electrocorticogram (ECoG) and intracellular recordings are progressively slowed down, leading to a steady isoelectric profile. This extreme brain state, putting the rat into a deep comatose, was carefully monitored by measuring the physiological constants of the animal throughout the experiments. Intracellular recordings allowed us to characterize and compare the integrative properties of the same neuron embedded into physiologically relevant cortical dynamics, such as those encountered in the sleep-wake cycle, and when the brain was fully silent.

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)0.001
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 score0.019
Threshold uncertainty score0.655

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.055
GPT teacher head0.368
Teacher spread0.313 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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