Spikoder: Dual‐Mode Graphene Neuron Circuit for Hardware Intelligence
Bibliographic record
Abstract
Neuromorphic computing envisions the realization of a hardware neural network mimicking the brain's energy efficiency and rapid information processing. Leveraging the multifunctional capabilities of core neuromorphic building blocks offers an efficient approach to developing compact and intelligent hardware systems. This article demonstrates a novel technique to employ a graphene memristor‐based leaky integrate‐and‐fire circuit, which is hereby referred to as Spikoder. This circuit not only functions as a dynamic encoder transforming continuous input signals into spike sequences but also operates as a neuron circuit with reduced topological complexity. The circuit exhibits exceptional spike‐encoding performance, validated using spike‐encoded images from the Modified National Institute of Standards and Technology dataset. The effectiveness of this encoding technique is evaluated through experiments on single‐layer and double‐layer fully connected spiking neural networks (SNNs). The single‐layer SNN utilizing the dual‐mode neuron circuit achieves a high image recognition accuracy of 90.77%, while the implementation of a double‐layer SNN increases the test accuracy to 97.37%, further demonstrating its scalability for high‐level neuromorphic computing. This research highlights the possibility of using the hybrid encoder‐neuron circuit as an efficient and scalable solution for advanced neuromorphic computing hardware.
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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".