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Thermodynamic model for memory

2024· article· en· W4399489854 on OpenAlexfundno aff
Qinyi Zhao

Bibliographic record

VenueBiosystems · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Memory and Neural Computing
Canadian institutionsnot available
FundersDepartment of Biology, University of DaytonMemorial University of NewfoundlandMcGill University
KeywordsComputer science

Abstract

fetched live from OpenAlex

A thermodynamic model for memory formation is proposed. Key points include: 1) Any thought or consciousness corresponds to a thermodynamic system of nerve cells. 2) The system concept of nerve cells can only be described by thermodynamics of condensed matter. 3) The memory structure is logically associated with the system structure or the normal structure of biology. 4) The development of our thoughts is processed irreversibly, and numerous states or thoughts can be generated. 5) Memory formation results from the reorganization and change of cellular structures (or memory structures), which are related to nerve cell skeleton and membrane. Their alteration can change the excitability of nerve cells and the pathway of neural impulse conduction. 6) Amnesia results from the loss of thermodynamic stability of the memory structure, which can be achieved by different ways. Some related phenomena and facts are discussed. The analysis shows that thermodynamics can account for the basic properties of memory.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.004
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0150.002

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.

Opus teacher head0.022
GPT teacher head0.245
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2024
Admission routes1
Has abstractyes

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