Soft Biomaterials Based Flexible Artificial Synapse for Neuromorphic Computing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 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) | 0.000 | 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 it