Soft Biomaterials Based Flexible Artificial Synapse for Neuromorphic Computing
Bibliographic record
Abstract
Abstract Artificial synapses are vital for neuromorphic computing chips that can potentially revolutionize von Neumann systems. Biomaterials‐based bio‐memristors have been investigated as synaptic emulators to develop neuromorphic computing chips due to their biocompatibility, degradability, flexibility, and low costs. However, the existing biomaterials‐based artificial synapses suffer from limited biological synapse functions, insufficient reliability, and poor endurance. Particularly, protein‐based artificial synapse with stable synaptic performances of long‐term potentiation/depression (LTP/LTD) for neuromorphic computing is challenging. Here, the soft material of egg albumen@CuO are employed to develop an artificial synapse. The device can mimic bio‐synaptic functionalities, including the excitatory postsynaptic current (EPSC), spike‐number‐dependent plasticity (SNDP), paired‐pulse facilitation (PPF), and LTP/LTD. High accuracy of 95% has been obtained by the neuromorphic computing simulation for pattern recognition. Combining with density functional theory calculations, multiphysics simulations, and electrical measurements, the analog resistive switching mechanism is attributed to electron hopping. In addition, the device is flexible and can be used to develop wearable systems. The results shed light on the biocompatible and wearable neuromorphic computing chips.
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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".