MétaCan
Menu
Back to cohort
Record W2071323465 · doi:10.3389/fneng.2011.00008

Processing Semblances Induced through Inter-Postsynaptic Functional LINKs, Presumed Biological Parallels of K-Lines Proposed for Building Artificial Intelligence

2011· article· en· W2071323465 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

VenueFrontiers in Neuroengineering · 2011
Typearticle
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsParallelsComputer sciencePostsynaptic potentialArtificial intelligenceNeuroscienceCognitive sciencePsychologyBiologyEngineering

Abstract

fetched live from OpenAlex

The internal sensation of memory, which is available only to the owner of an individual nervous system, is difficult to analyze for its basic elements of operation. We hypothesize that associative learning induces the formation of functional LINK between the postsynapses. During memory retrieval, the activation of either postsynapse re-activates the functional LINK evoking a semblance of sensory activity arriving at its opposite postsynapse, nature of which defines the basic unit of internal sensation - namely, the semblion. In neuronal networks that undergo continuous oscillatory activity at certain levels of their organization re-activation of functional LINKs is expected to induce semblions, enabling the system to continuously learn, self-organize, and demonstrate instantiation, features that can be utilized for developing artificial intelligence (AI). This paper also explains suitability of the inter-postsynaptic functional LINKs to meet the expectations of Minsky's K-lines, basic elements of a memory theory generated to develop AI and methods to replicate semblances outside the nervous system.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.525
Threshold uncertainty score0.784

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 communication0.0000.000
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 head0.127
GPT teacher head0.279
Teacher spread0.153 · 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