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Attractors of cooperative dynamical systems are particularly simple; for example, a nontrivial periodic orbit cannot be an attractor. This paper provides characterizations of attractors for the wider class of coherent systems, defined by…

Dynamical Systems · Mathematics 2007-10-19 David Angeli , Morris W. Hirsch , Eduardo D. Sontag

In this paper a Lorenz-like system, describing the process of rotating fluid convection, is considered. The present work demonstrates numerically that this system, also like the classical Lorenz system, possesses a homoclinic trajectory and…

Chaotic Dynamics · Physics 2015-11-17 G. A. Leonov , N. V. Kuznetsov , T. N. Mokaev

A complex network is a condensed representation of the relational topological framework of a complex system. A main reason for the existence of such networks is the transmission of items through the entities of these complex systems. Here,…

Physics and Society · Physics 2018-04-18 María Pereda , Ernesto Estrada

The deterministic dynamics of randomly connected neural networks are studied, where a state of binary neurons evolves according to a discreet-time synchronous update rule. We give a theoretical support that the overlap of systems' states…

Statistical Mechanics · Physics 2015-03-10 Taro Toyoizumi , Haiping Huang

We prove that the complex conjugate (c.c.) eigenvalues of a smoothly varying real matrix attract (Eq. 15). We offer a dynamical perspective on the motion and interaction of the eigenvalues in the complex plane, derive their governing…

Spectral Theory · Mathematics 2016-02-26 Ramis Movassagh

We study a family of networks of autocatalytic reactions, which we call hyperchains, that are a generalization of hypercycles. Hyperchains, and the associated dynamical system called replicator equations, are a possible mechanism for…

Dynamical Systems · Mathematics 2021-04-23 Badal Joshi , Gheorghe Craciun

In most networks, the connection between a pair of nodes is the result of their mutual affinity and attachment. In this letter, we will propose a Mutual Attraction Model to characterize weighted evolving networks. By introducing the initial…

Disordered Systems and Neural Networks · Physics 2009-11-11 Wen-Xu Wang , Bo Hu , Bing-Hong Wang , Gang Yan

In this paper we present a comprehensive mechanism for the emergence of strange attractors in a two-parametric family of differential equations acting on a three-dimensional sphere. When both parameters are zero, its flow exhibits an…

Dynamical Systems · Mathematics 2020-05-19 Alexandre A. P. Rodrigues

There are few explicit examples in the literature of vector fields exhibiting complex dynamics that may be proved analytically. We construct explicitly a {two parameter family of vector fields} on the three-dimensional sphere $\EU^3$, whose…

Dynamical Systems · Mathematics 2015-06-15 Alexandre A. P. Rodrigues , Isabel S. Labouriau

The paper proposes two dynamical systems based on the generalized Lotka-Volterra model of three excitable elements interacting through excitatory couplings. It is shown that for some values of the coupling parameters in the phase space of…

Chaotic Dynamics · Physics 2023-12-11 A. G. Korotkov , S. Y. Zagrebin , G. V. Osipov

The paper introduces a new 3D strange attractor topologically different from any other known chaotic attractors. The intentionally constructed model of three autonomous first-order differential equations derives from the coupling-induced…

Chaotic Dynamics · Physics 2012-04-03 Safieddine Bouali

We clarify the connection between attractor solutions known from studies of Bjorken flow in conformal models of relativistic fluid dynamics and the more general description of attractors as submanifolds in phase space. We show how to…

Nuclear Theory · Physics 2025-08-29 Michał Spaliński

The replicator equations are a family of ordinary differential equations that arise in evolutionary game theory, and are closely related to Lotka-Volterra. We produce an infinite family of replicator equations which are Liouville-Arnold…

Dynamical Systems · Mathematics 2023-06-07 Josh Paik , Christopher Griffin

Neural networks transform high-dimensional data into compact, structured representations, often modeled as elements of a lower dimensional latent space. In this paper, we present an alternative interpretation of neural models as dynamical…

Machine Learning · Computer Science 2026-03-26 Marco Fumero , Luca Moschella , Emanuele Rodolà , Francesco Locatello

Networks of interacting nodes connected by edges arise in almost every branch of scientific enquiry. The connectivity structure of the network can force the existence of invariant subspaces, which would not arise in generic dynamical…

Dynamical Systems · Mathematics 2022-02-23 Claire M. Postlethwaite , Rob Sturman

Consider a holomorphic automorphism acting hyperbolically on an invariant compact set. It has been conjectured that the arising stable manifolds are all biholomorphic to Euclidean space. Such a stable manifold is always equivalent to the…

Complex Variables · Mathematics 2014-08-05 Han Peters , Iris Marjan Smit

Heteroclinic connections are trajectories that link invariant sets for an autonomous dynamical flow: these connections can robustly form networks between equilibria, for systems with flow-invariant spaces. In this paper we examine the…

Dynamical Systems · Mathematics 2019-09-04 Peter Ashwin , Sofia Castro , Alexander Lohse

We consider a heteroclinic network in the framework of winnerless competition of species. It consists of two levels of heteroclinic cycles. On the lower level, the heteroclinic cycle connects three saddles, each representing the survival of…

Adaptation and Self-Organizing Systems · Physics 2018-12-26 Maximilian Voit , Hildegard Meyer-Ortmanns

The paper deals with a multiple species Lotka-Volterra model with infinite distributed delays and feedback controls, for which we assume a weak form of diagonal dominance of the instantaneous negative intra-specific terms over the infinite…

Classical Analysis and ODEs · Mathematics 2013-07-29 Teresa Faria , Yoshiaki Muroya

We present results from numerical studies of supervised learning operations in recurrent networks considered as graphs, leading from a given set of input conditions to predetermined outputs. Graphs that have optimized their output for…

Adaptation and Self-Organizing Systems · Physics 2007-05-23 H. Atmanspacher , T. Filk