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Record W2062020955 · doi:10.4018/ijaras.2014100104

Spatial Multiplexing

2014· article· en· W2062020955 on OpenAlexaff
Thomy Nilsson

Bibliographic record

VenueInternational Journal of Adaptive Resilient and Autonomic Systems · 2014
Typearticle
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsBottleneckMultiplexingComputer scienceSpatial analysisSpatial multiplexingDistributed computingNeuroscienceComputer networkBiologyGeographyTelecommunicationsEmbedded systemRemote sensing

Abstract

fetched live from OpenAlex

Geometrical constraints limit how much information can be received and emitted along real pathways across the boundary of any processor. Applied to central nervous systems this imposes a seemingly impassable bottleneck to the evolution of large brains. A small brain could never access enough information to warrant a larger brain. A small brain could not send enough information to operate a large body. Larger bodies are needed to support larger brains. Thus, with a rare exception, there are no invertebrates with large brains or large bodies. It is proposed that a convergent-divergent scanning neural network developed which enabled vertebrates to squeeze more information through this bottleneck by “spatial multiplexing”. This reduces the number of pathways into, between and from processors by a factor of 16 while maintaining spatial and intensity accuracy. This paper describes spatial multiplexing using downloadable spreadsheet models and shows how the necessity of scanning likely introduced brain rhythms.

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)0.000
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 score0.872
Threshold uncertainty score0.271

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 head0.024
GPT teacher head0.254
Teacher spread0.230 · 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 designSimulation or modeling
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

Citations1
Published2014
Admission routes1
Has abstractyes

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