Equivalence of stationary dynamical solutions in a directed chain and a Delay Differential Equation of neuroscientific relevance
Giulio Colombini, Nicola Guglielmi, Armando Bazzani

TL;DR
This paper demonstrates that stationary dynamical states in a directed neural network cycle can be effectively modeled by a delay differential equation, revealing a solitonic wave behavior and preserving key features of the original system.
Contribution
It introduces a novel mapping between a directed neural cycle's stationary states and a single-delay differential equation model, highlighting the solitonic nature of wave states.
Findings
Stationary states emerge as self-sustained traveling waves.
The DDE model accurately reproduces bifurcation behavior.
Wave propagation exhibits solitonic scaling in large chains.
Abstract
While synchronized states, and the dynamical pathways through which they emerge, are often regarded as the paradigm to understand the dynamics of information spreading on undirected networks of nonlinear dynamical systems, when we consider directed network architectures, dynamical stationary states can arise. To study this phenomenon we consider the simplest directed network, a single cycle, and excitable FitzHugh-Nagumo (FHN) neurons. We show numerically that a stationary dynamical state emerges in the form of a self-sustained traveling wave, through a saddle-point bifurcation of limit cycles that does not destabilize the global fixed point of the system. We then formulate an effective model for the dynamical steady state of the cycle in terms of a single-neuron Delay Differential Equation (DDE) featuring an explicitly delayed feedback, demonstrating numerically the possibility 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
TopicsNonlinear Dynamics and Pattern Formation · stochastic dynamics and bifurcation · Neural Networks Stability and Synchronization
