Realization of Modified Electrical Equivalent of Memristor-Based Pavlov’s Associative Learning to Avoid Training Fallacies
Bibliographic record
Abstract
Biological systems learn from past experiences by establishing relationships between two simultaneously occurring events, a phenomenon known as associative learning. This concept has promising applications in modern AI (Artificial Intelligence) and ML (Machine Learning). To leverage it effectively, a precise electrical model that can simulate associative learning observed in biological systems is essential. The paper focuses on modeling Pavlov’s famous experiment related to the drooling of dogs at the sound of bell after associating the food with the bell during training. The study addresses limitations in existing circuit designs that fail to accurately replicate associative learning in dogs, particularly when the sequence of food and bell signals deviates from a specific pattern. We propose a novel design using a few CMOS (Complementary Metal Oxide Semiconductor) transistors and memristor models that produces an output corresponding to the dogs drooling only when food and bell signals are associated, mirroring real-life training conditions. The results section first discusses simulations using the standard TiO2 (Titanium Oxide) memristor model, followed by experimental results obtained from a classical memristor emulator. Both simulation and experimental findings confirm the effectiveness of the circuit designs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".