Heterogeneous stimuli induced nonassociative learning behavior in ZnO nanowire memristor
Bibliographic record
Abstract
Nonassociative learning is a biologically essential and evolutionarily adaptive behavior in organisms. The bionic simulation of nonassociative learning based on electronic devices is essential to the neuromorphic computing. In this work, nonassociative learning is mimicked by a ZnO nanowire memristor without any other peripheral control circuit. The memristor demonstrates habituation and sensitization behaviors at the electrical and optical stimuli. Typical network-level parametric characteristics of habituation in neuroscience are realized in the memristor. When the heterogeneous stimuli are applied coincidentally, sensitization pulse could be identified by the exceptional response current. The results show that the natural selection rules could be simulated by the current single memristor. A possible mechanism based on the trapping states and adsorption of oxygen at the interface of Au/ZnO is proposed. The implementation of nonassociative learning in a single memristor device paves the way for building neuromorphic systems by simple electronic devices.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".