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Record W2600440520 · doi:10.1371/journal.pone.0174621

Phase diagrams and dynamics of a computationally efficient map-based neuron model

2017· article· en· W2600440520 on OpenAlex
Mauricio Girardi‐Schappo, G. S. Bortolotto, Rafael V. Stenzinger, Jheniffer J. Gonsalves, M. H. R. Tragtenberg

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePLoS ONE · 2017
Typearticle
Languageen
FieldComputer Science
TopicNeural Networks and Applications
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersFundação de Amparo à Pesquisa e Inovação do Estado de Santa CatarinaConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsAperiodic graphParameter spaceDynamical systems theoryBifurcationStatistical physicsBiological neuron modelPhase spaceComputer sciencePhysicsPhase diagramPerceptronAlgorithmBiological systemPhase (matter)Artificial intelligenceArtificial neural networkMathematicsBiologyGeometryCombinatoricsNonlinear system

Abstract

fetched live from OpenAlex

We introduce a new map-based neuron model derived from the dynamical perceptron family that has the best compromise between computational efficiency, analytical tractability, reduced parameter space and many dynamical behaviors. We calculate bifurcation and phase diagrams analytically and computationally that underpins a rich repertoire of autonomous and excitable dynamical behaviors. We report the existence of a new regime of cardiac spikes corresponding to nonchaotic aperiodic behavior. We compare the features of our model to standard neuron models currently available in the literature.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.454
Threshold uncertainty score0.223

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 head0.049
GPT teacher head0.266
Teacher spread0.217 · 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