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Record W2123027306 · doi:10.5555/2693848.2694278

Neuron time warp

2014· article· en· W2123027306 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

VenueWinter Simulation Conference · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGene Regulatory Network Analysis
Canadian institutionsMcGill University
Fundersnot available
KeywordsComputer scienceNeuronFidelityMonte Carlo methodScale (ratio)Biological neuron modelBiological systemArtificial intelligenceArtificial neural networkNeuroscienceMathematicsPhysics

Abstract

fetched live from OpenAlex

Detailed simulation of chemical reactions and the diffusion of ions through a neuronal membrane presents challenges due to the multiple scales at which this occurs, scales that require development and consolidation of a number of different simulation methodologies. In this paper, we describe Neuron Time Warp (NTW), a part of the NEURON project for development of multi-scale tools for simulations of brain parts and brains. NTW relies upon the Next Subvolume Method, a stochastic Monte Carlo algorithm used to simulate chemical reactions within the membrane of a neuron. We make use of a model of a dendrite branch on which to evaluate NTW's performance using MPI and shared memory on a multi-core machine. This work is a first step towards the development of multi-scale simulation models which are capable of portraying the behavior of a neuron with greater fidelity then is possible with differential equation based models alone.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.563
Threshold uncertainty score0.425

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 head0.011
GPT teacher head0.242
Teacher spread0.231 · 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