Bibliographic record
Abstract
In this work, we attempt to provide a theoretical rationale, rooted on biological grounds, for the anatomical organization of neurons in the brain. We propose a maximum entropy principle for the brain, inspired by Merzenich's work on neuroplasticity and Hebb's and Edelman's postulate for brain development. We show that when one maximizes the joint entropy of neuronal diameter and length constrained by neurophysiological elements of power, resource, space, and time, one arrives at a maximum entropy joint distribution conforming to a joint gamma distribution often reported in the literature. Then, using graph theory, we show how biological neuronal networks may self-organize topologically to maximize entropy and exhibit properties of a Rentian scaled small-world architecture to accompany learning and memory formation often observed in in vivo and in vitro neuronal networks across different species. This thesis is an attempt to show how the brain unfolds in a structured and lawful manner such that it embeds these laws into its anatomy to make learning and memory formation possible. It attempts to show that these fundamental elements of everyday life are mapped to the structural and topological level, which can be explained through an inference model from maximizing Shannon's entropy subject to biological constraints. In the hope of advancing the engineering of truly intelligent robotics, we believe that translating brain dynamics with mathematical and physical laws can pave the way for the future.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".