Optically Controlled P–Cu<sub><i>x</i></sub>O-Based Artificial Synaptic Device for Neuromorphic Applications
Bibliographic record
Abstract
Memristor-based optoelectronic artificial synapses have a great potential to enhance the efficiency of future neuromorphic computing. Like neurons of the retina, they have the potential to enable real-time visual preprocessing. This highlights the growing importance of improving optoelectronic artificial synapses for next-generation neuromorphic computing and neuromorphic visual systems. These artificial synapses can enhance neuromorphic visual systems, extending their capabilities beyond visible light. This study introduces a P-type copper oxide-based optical memristor device that exhibits fundamental biosynaptic characteristics like long-term potentiation (LTP) and long-term depression (LTD), which can be tuned using optical stimuli. These LTP/LTD characteristics were used as weights in a single-layer perceptron neural network to classify the MNIST data set using an off-chip training algorithm. We also demonstrated light-induced short-term plasticity and optical paired-pulse facilitation, which are the two important characteristics of neurons of the human retina that help in image preprocessing. We also implemented Pavlovian conditioning on the device using a combination of electrical and optical stimuli. These results indicate the possibility of using this device as an optically controlled artificial synaptic device for neuromorphic vision sensor applications.
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 | 0.001 | 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".