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Record W2086271267 · doi:10.1080/00207450701769323

Analysis of the Selective Nature of Sensory Nerve Stimulation Using Different Sinusoidal Frequencies

2008· article· en· W2086271267 on OpenAlexaff
Matthew Langille, José A. González-Cueto, Swarna Sundar

Bibliographic record

VenueInternational Journal of Neuroscience · 2008
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsDalhousie University
Fundersnot available
KeywordsStimulationNeuroscienceSIGNAL (programming language)ChemistrySensory systemNerve fiberElectrophysiologyFiberPsychologyComputer science

Abstract

fetched live from OpenAlex

This study examines the ability to selectively activate different nerve fibers in a finger by using different sinusoidal stimulation frequencies. Specifically, the stimulation of A-beta, A-delta, and C-fibers is looked into, and responses from each of three different stimuli (5 Hz, 250 Hz, and 2000 Hz) are compared. Action potential (AP) responses from the different nerve fibers are simulated. Activation thresholds are determined for each fiber type. The resulting firing frequencies are compared with thresholds found in the literature to determine the stimulating signal amplitude at which sensations begin to be perceived for each stimulation frequency. Results indicate that while selective stimulation of C-fibers and A-beta fibers appears to be possible with 5 Hz and 2000 Hz, respectively, selective stimulation of A-delta fibers may not be possible due to them requiring a higher stimulating signal amplitude to cause the nerve to reach the physiological threshold than A-beta fibers for 250 Hz. Thus, selective stimulation of the three types of nerve fibers may not be possible for all three examined frequencies.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.222
Threshold uncertainty score0.358

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.056
GPT teacher head0.311
Teacher spread0.254 · 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

Citations17
Published2008
Admission routes1
Has abstractyes

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