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Record W1977984641 · doi:10.1142/s0218127404011016

LINEAR STABILITY AND HOPF BIFURCATION IN A TWO-NEURON NETWORK WITH THREE DELAYS

2004· article· en· W1977984641 on OpenAlexaff
Shangjiang Guo, Lihong Huang, Lin Wang

Bibliographic record

VenueInternational Journal of Bifurcation and Chaos · 2004
Typearticle
Languageen
FieldComputer Science
TopicNeural Networks Stability and Synchronization
Canadian institutionsMemorial University of Newfoundland
FundersNational Science Foundation
KeywordsHopf bifurcationCenter manifoldMathematicsPitchfork bifurcationSaddle-node bifurcationStability (learning theory)Transcritical bifurcationLinear stabilityBiological applications of bifurcation theoryBifurcationBogdanov–Takens bifurcationBifurcation diagramMathematical analysisControl theory (sociology)InstabilityNonlinear systemPhysicsComputer scienceMechanics

Abstract

fetched live from OpenAlex

Linear stability and Hopf bifurcation in a two-neuron network with three delays (one is due to the self-feedback) are investigated. Based on the normal form approach and the center manifold theory, we derive the formula to determine the direction and stability of Hopf bifurcation.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

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 studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.015
GPT teacher head0.255
Teacher spread0.240 · 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 designSimulation or modeling
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

Citations60
Published2004
Admission routes1
Has abstractyes

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