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Record W2576309903 · doi:10.1098/rstb.2016.0158

Integrating Hebbian and homeostatic plasticity: the current state of the field and future research directions

2017· article· en· W2576309903 on OpenAlexaff
Tara Keck, Taro Toyoizumi, Lu Chen, Brent Doiron, Daniel E. Feldman, Kevin Fox, Wulfram Gerstner, Philip G. Haydon, Mark Hübener, Hey‐Kyoung Lee, John Lisman, Tobias Rose, Frank Sengpiel, David Stellwagen, Michael P. Stryker, Gina G. Turrigiano, Mark C. W. van Rossum

Bibliographic record

VenuePhilosophical Transactions of the Royal Society B Biological Sciences · 2017
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsMcGill University
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute on Drug AbuseMedical Research CouncilNational Institute of Neurological Disorders and StrokeNational Eye InstituteBiotechnology and Biological Sciences Research Council
KeywordsHebbian theoryCurrent (fluid)Field (mathematics)Homeostatic plasticityPlasticityState (computer science)Computer sciencePsychologyNeuroscienceMetaplasticityArtificial intelligenceEngineeringSynaptic plasticityMaterials scienceArtificial neural networkBiologyMathematicsElectrical engineering

Abstract

fetched live from OpenAlex

We summarize here the results presented and subsequent discussion from the meeting on Integrating Hebbian and Homeostatic Plasticity at the Royal Society in April 2016. We first outline the major themes and results presented at the meeting. We next provide a synopsis of the outstanding questions that emerged from the discussion at the end of the meeting and finally suggest potential directions of research that we believe are most promising to develop an understanding of how these two forms of plasticity interact to facilitate functional changes in the brain.This article is part of the themed issue 'Integrating Hebbian and homeostatic plasticity'.

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)0.026
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.026
Threshold uncertainty score0.136

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0020.003
Science and technology studies0.0020.012
Scholarly communication0.0110.036
Open science0.0040.005
Research integrity0.0110.012
Insufficient payload (model declined to judge)0.0110.002

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 head0.099
GPT teacher head0.367
Teacher spread0.268 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations238
Published2017
Admission routes1
Has abstractyes

Explore more

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