A Transmissive Theory of Brain Function: Implications for Health, Disease, and Consciousness
Bibliographic record
Abstract
Identifying a complete, accurate model of brain function would allow neuroscientists and clinicians to make powerful neuropsychological predictions and diagnoses as well as develop more effective treatments to mitigate or reverse neuropathology. The productive model of brain function, which has been dominant in the field for centuries, cannot easily accommodate some higher-order neural processes associated with consciousness and other neuropsychological phenomena. However, in recent years, it has become increasingly evident that the brain is highly receptive to and readily emits electromagnetic (EM) fields and light. Indeed, brain tissues can generate endogenous, complex EM fields and ultraweak photon emissions (UPEs) within the visible and near-visible EM spectra. EM-based neural mechanisms, such as ephaptic coupling and non-visual optical brain signaling, expand canonical neural signaling modalities and are beginning to disrupt conventional models of brain function. Here, we present an evidence-based argument for the existence of brain processes that are caused by the transmission of extracerebral, EM signals and recommend experimental strategies with which to test the hypothesis. We argue for a synthesis of productive and transmissive models of brain function and discuss implications for the study of consciousness, brain health, and disease.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".