Bibliographic record
Abstract
Spiking neuron models are inspired by biological neurons. They can simulate the neuronal activities of the mammalian brains, such as spiking (integrator) and periodic oscillation (resonator). A spiking neural network consisting of a cluster of spiking neurons can be used to simulate the collective dynamic behaviors of a brain neural network. This paper presents step-by-step analyses for the non-linear dynamics of mathematical spiking neuron models and sets forth a novel spiking model based on logistic function using an analytical approach. The logistic function is a well-known one-dimensional dynamical system and can generate spiking or periodic oscillation based on the system parameter. The novel spiking neural model is a combination of the integrate-and-fire and the quadratic integrate-and-fire neuron models with an added parameter to control the neural dynamics in order to generate stable, periodic, or chaotic neural behavior with flexibility. The analytical approach presented in this paper can be applied extensively to the design and analyses of multi-dimensional neuron models. The goal of this research project is to understand the dynamical behaviors of biological neurons in order to design biologically inspired spiking neuron model for building artificial intelligence, treating cognitive disorders, and advancing the scientific frontiers of brain research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".