Evaluating the Signal Processing Capacities of Post-Mortem Cerebral Cortical Tissue by Artificial Phototransduction of Dynamic Visual Stimuli
Bibliographic record
Abstract
The signal processing function of human cerebral cortical tissues is determined by the regional cytoarchitectures distributed throughout the brain. Based upon this assumption, we pursued the hypothesis that residual microstructure within the primary and associative visual cortices of a fixed, post-mortem whole human brain would process electrical signals differentially. To this end, we designed and engineered a very simple brain-photocell interface. Photostimuli, presented as either periodic flashes or as dynamic visual images, were transduced by photocells attached to the optic nerve of a post-mortem human brain specimen. The novel approach revealed that microvolt fluctuations within the primary and associative visual cortices could be discriminated. Simple light-dark discrimination was noted for the primary visual area (BA17) whereas within the right occipito-parietal cortices of the dorsal visual stream (BA19, BA7), spectral power of microvolt fluctuations could discriminate moving visual stimuli from those which were non-moving. Discriminant analysis classified movement represented within the right parietal lobe with 80% success. Together, the results suggest that artificially generated electrical signals are processed differentially by alternative cortical regions in the post-mortem brain.
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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 | 0.000 |
| 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 | 0.000 |
| 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".