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Record W1964812021 · doi:10.1103/physrevlett.92.080601

Noise Shaping by Interval Correlations Increases Information Transfer

2004· article· en· W1964812021 on OpenAlex

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

VenuePhysical Review Letters · 2004
Typearticle
Languageen
FieldPhysics and Astronomy
Topicstochastic dynamics and bifurcation
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsInterval (graph theory)Noise (video)Statistical physicsInformation transferPhysicsSpectral densitySIGNAL (programming language)Point processSpike (software development)Information transmissionTransmission (telecommunications)Transfer (computing)Point (geometry)Power (physics)Computer scienceMathematicsStatisticsTelecommunicationsQuantum mechanicsArtificial intelligenceCombinatorics

Abstract

fetched live from OpenAlex

The influence of intrinsic firing interspike interval correlations on the noise spectrum and information transfer is studied. This is done through the comparison of two simple firing models, one of which is a renewal process while the other displays interval correlations. These correlations are shown to shape the spike train power spectrum and, in particular, to decrease the noise power at low frequencies. Linear response theory and numerical simulations reveal how this shaping can increase the transmission of information about a time-varying signal. Our results are relevant to the analysis of nonrenewal point processes and signal detection in physics and biology.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.635
Threshold uncertainty score0.419

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 head0.010
GPT teacher head0.256
Teacher spread0.246 · 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