Adjustable Leaky-Integrate-and-fire neurons based on memristor-coupled capacitors
Bibliographic record
Abstract
To address the von Neumann bottleneck, artificial neural networks (ANNs) are aroused to construct neuromorphic computing systems. The artificial neuron is one of the essential components that collect the weight updating information of artificial synapses. Leaky-Integrate-and-Fire (LIF) neuron mimicking the cell membrane of biological neurons is a promising neural model due to its simplicity. To adjust the performances of artificial neurons, multiple resistors with different resistive values need to be integrated into the circuit. Whereas more components mean higher manufacturing costs, more complex circuits, and more complicated control systems. In this work, the first adjustable LIF neuron was developed, which can further simplify the circuits. To achieve adjustable fashions, a memristor-coupled capacitor with binary intrinsic resistant states was employed to integrate input signals. The intrinsic tunable resistance can modify the charge leaking rate, which determines the neural spiking features. Another contribution of this work is to overcome the hinder of credible circuit design using novel memristor-coupled capacitors with entangled capacitive and memristive effects. The genetic algorithm (GA) was utilized to detach the entanglement of memristive and capacitive effects, which is crucial for circuit design. This method can be generalized to other entangled physical behaviors, facilitating the development of novel circuits. The results will not only strengthen neuromorphic computing capability but also provides a methodology to mathematically decode electronic devices with entangled physical behaviors for novel circuits.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".