Processing Semblances Induced through Inter-Postsynaptic Functional LINKs, Presumed Biological Parallels of K-Lines Proposed for Building Artificial Intelligence
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".