Experimental Evidence of Classical Conditioning and Microscopic Engrams in an Electroconductive Material
Bibliographic record
Abstract
Synthetic experimental substrates are indispensable tools which can allow researchers to model biological processes non-invasively in three-dimensional space. In this study, we investigated the capacities of an electroconductive material whose properties converge upon those of the brain. An electrically conductive material composed of carbohydrates, proteins, fats, ions, water, and trace amounts of other organic compounds and minerals was classically conditioned as inferred by electrophysiological measurements. Spectral densities evoked during the display of a conditioned stimulus (CS) probe were strongly congruent with those displayed during the conditioned-unconditioned stimulus pairing (CS-UCS). The neutral stimulus consisted of the pulsed light from a LED. The unconditioned stimulus was an alternating current. Interstimulus intervals >130 ms did not result in conditioned responses. Microscopic analysis of the chemically-fixed substratum revealed 10-200 μm wide 'vessel structures' within samples exposed to a stimulus. Greater complexity (increased fractal dimensions) was clearly discernable by light microscopy for stained sections of fixed samples that had been conditioned compared to various controls. The denser pixels indicated greater concentration of stain and increased canalization. Implications for learning and memory formation are discussed.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".