A topological connectome surface network with spin-half particles can produce brain-like signals and store memory
Bibliographic record
Abstract
Abstract In this paper we use the methods of theoretical physics to show how brain-like signals can be generated in a special surface network with the topological connectivity of the biological brain by exploiting its form. The network is required to have surface spin-half particles. We show that the signals thus generated carry information regarding their creation and can transfer and store this information to form memory structures in helical aligned surface spin-half particles present on the surfaces of the pathways traversed by the signals. Theoretical neuroscience is progressing strongly with novel representations of the brain, enhanced by the increase of computational power now available. New methods to explore complex brain events and the structures for storing memories as engrams are emerging. However, there are major conceptual theoretical problems that remain unaddressed. Current theoretical methods are very capable of reacting to experimental results and modelling both neural signalling and structure. Yet they still fall short to throw light on how the brain creates its own information code, or relate the variety of brain signals observed, or explain where and how memories are stored. We prove that current brain signal interpretations cannot carry information regarding their formation so that they cannot be used to understand how memories of events are related to signals. Thus, our results address these basic unresolved theoretical problems of neuroscience and suggest testable solutions. The memory structures of aligned spin-half particles suggested have not been observed in biological organisms as yet they but they have been observed in solid state physics and their existence is consistent with conventional understandings of neurobiology. All the results stated follow from the dynamical law for the network.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".