Logical Resonance in Izhikevich Neuron
Bibliographic record
Abstract
This paper proposes a new logic element model based on an Izhikevich (IZ) neuron and neural system that emulates two- and three-state logic behaviours. In a noise-free environment, with a periodic current of suitable amplitude and frequency, the IZ system is capable of performing logical AND and OR operations. Initially, a single IZ neuron demonstrates membrane dynamics in response to an input signal generated by combining two-state logic currents below the threshold. Subsequently, an IZ neural system model is introduced to enhance the reliability and resilience of the system. This model is characterised by electrical coupling with fast conduction and chemical coupling with a more adaptable structure. Each logic input independently influences each neuron within the system. Additionally, it has been observed that the reliability of the logic element is influenced by changes in synaptic strength, with a neural system lacking sufficient synaptic strength failing to generate logical output. Furthermore, the system displays a three-state logic behaviour under suitable forcing periodicity, thus enhancing the power efficiency of the logic element. The proposed IZ neuron and neural system are expected to significantly impact the development of brain-inspired logic elements.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".