Quantum mechanisms in the brain: from conjectures and theories to experimental evidence
Bibliographic record
Abstract
As the emerging field of quantum biology matures it is becoming possible to understand the fundamental workings of biological systems at the subatomic level. Understanding the complex function of the brain from this vantage point is no exception. Looking at the subcellular functions of neurons beyond the standard classical mechanisms of chemical and electrical signaling offers the potential to design advanced diagnostics and therapeutics for neurological disorders and mental illnesses based on quantum technologies and could even lead to a more comprehensive understanding of human cognition, behavior, or even consciousness itself. Towards this end there have been many theories proposed for how quantum mechanics may play a role in brain function. However, regardless of how promising these theories are, without experimental evidence to provide validation they are merely interesting conjectures. Here we present a review of current theoretical quantum mechanisms proposed to influence brain function (e.g., spin-based mechanisms, optical mechanisms) and place them in the context of existing experimental evidence. We find that experimental evidence does support quantum mechanisms that may exist in the brain, but significant challenges remain in explaining how such mechanisms may influence processes on relevant biological lengths and timescales. Potential approaches to address these challenges are also discussed.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.027 |
| Scholarly communication | 0.006 | 0.017 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| 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".