Bifurcation analysis and multistability detection of two delay-coupled FHN neurons
Niloofar Farajzadeh Tehrani, MohammadReza Razvan

TL;DR
This study analyzes the complex dynamics of two delay-coupled FitzHugh-Nagumo neurons, revealing bifurcation phenomena, multistability, and various oscillatory behaviors influenced by coupling strength and delay.
Contribution
It provides a comprehensive bifurcation analysis of coupled non-identical neurons with delay, identifying new stability regions and multistability patterns not previously characterized.
Findings
Identification of bifurcation points including saddle-node, transcritical, Hopf, and Bautin bifurcations.
Discovery of multistability with synchronous and anti-phase solutions.
Delay-dependent stability regions and complex bifurcation structures.
Abstract
This paper presents an investigation of the dynamics of two coupled non-identical FitzHugh-Nagumo neurons with quadratic term and delayed synaptic connection. We consider coupling strength and time delay as bifurcation parameters, and try to classify all possible dynamics. Bifurcation diagrams are obtained numerically or analytically from the mathematical model, and the parameter regions of different behaviors are clarified. The neural system exhibits a unique rest point or three ones by employing the saddle-node bifurcation, when strong coupling is applied in the system. Also the trivial rest point shows transcritical bifurcation with one of the new rest points. The asymptotic stability and possible Hopf and Bautin bifurcations of the trivial rest point are studied by analyzing the corresponding characteristic equation. Fold cycle, torus, fold-torus, and big homoclinic bifurcations of…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
Topicsstochastic dynamics and bifurcation · Neural dynamics and brain function · Nonlinear Dynamics and Pattern Formation
