Synaptic learning behavior of a TiO <sub>2</sub> nanowire memristor
Bibliographic record
Abstract
Abstract TiO 2 nanowire memristors were fabricated by dielectrophoresis. The responding current of the memristor continuously increases and decreases with the consecutive positive and negative sweep voltage, which is similar to the nonlinear transmission characteristics of biological synapses. Spike-rate-dependent plasticity and learning behaviors of TiO 2 memristor were studied by applying programmed pulses. The pulses with higher amplitude, bigger width and smaller interval cause a larger excitatory postsynaptic current. The number of relearning pulses is decreased with the learning experience, and a deepening memory will be consolidated by the repeated learning process. A mechanism based on the oxygen vacancy migration is proposed for the learning behavior. Excess oxygen vacancies are generated during the learning process and the conducting pathways are formed by the vacancy drift under the applied voltage. After removing the voltage at the forgetting process, back diffusion and electron trapping of the oxygen vacancies dominate the relaxation time, and the metastable atoms are formed with the involvement of the oxygen atoms. However, weak chemical bonding among the metastable atoms leads to the migration of the regenerated oxygen vacancies again, contributing to the enhanced current in the relearning process.
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 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".