Dynamical Analysis and Synchronization of a New Memristive Chialvo Neuron Model
Bibliographic record
Abstract
Chialvo is one of the two-dimensional map-based neural models. In this paper, a memristor is added to this model to consider the electromagnetic induction’s effects. The memristor is defined based on a hyperbolic tangent function. The dynamical variations are analyzed by obtaining the bifurcation diagrams and Lyapunov spectra. It is shown that the most effective parameters on the dynamics are the magnetic strength and the injected current. The memristive Chialvo can exhibit different neural behaviors. It is also proven that, like the primary Chialvo model, the memristive version has coexisting attractors; an oscillating state coexists with a fixed point. In addition, to understand how memristive neurons behave in a network, two memristive Chialvo models are coupled with electrochemical synapses. By connecting two neurons and calculating the synchronization error, we can determine the system’s synchronizability. It is indicated that the electrical coupling is essential for the occurrence of complete synchronization in the network of memristive Chialvo, and the sole chemical coupling does not lead to synchronization.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".