Adjustable Leaky-Integrate-and-fire neurons based on memristor-coupled capacitors
Why this work is in the frame
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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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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 | 0.000 | 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) | 0.000 | 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