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Record W2771098781

A Circuit Model of Sensory Receptor Function

2017· article· en· W2771098781 on OpenAlexaff
Gerry Fung, Willy Wong

Bibliographic record

VenueCMBES Proceedings · 2017
Typearticle
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSensory systemComputer scienceBiological neural networkBiological neuron modelVery-large-scale integrationElectronic circuitArtificial neural networkNeuroscienceArtificial intelligenceMachine learningEngineeringPsychologyElectrical engineeringEmbedded system
DOInot available

Abstract

fetched live from OpenAlex

The main objective of this work is the design of a circuit that can mimic the activity of sensory receptors. Several integrate-and-fire models have been proposed for a generic neuron: Lapicque’s RC circuit, the Hodgkin and Huxley model, and various VLSI circuits. The first two models do not respond appropriately to time-varying stimuli. On the other hand, the problem with many VLSI circuits is that they are usually designed to demonstrate specific neural features, for a single neural pathway. The firing rate of a periphery sensory neuron can be explained by using a theoretical model based upon information transmission along the nerve fiber [1]. This model accounts for varying neural firing rates, and adaptation/deadaptation effects. By modifying an integrate-and-fire circuit, one can mimic the equivalent behaviour of this theoretical model.

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)0.001
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 score0.106
Threshold uncertainty score0.379

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.082
GPT teacher head0.264
Teacher spread0.182 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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