Corrigendum to: ``Sigmoidal approximations of Heaviside functions in neural lattice models

DOI10.1016/J.JDE.2020.11.017zbMath1452.34021OpenAlexW3098588725MaRDI QIDQ2217341

Xiaoying Han, Peter E. Kloeden

Publication date: 29 December 2020

Published in: Journal of Differential Equations (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jde.2020.11.017

zbMATH Keywords

global attractorsigmoidal functionHeaviside operatorneural field lattice modelupper semi convergence

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20) Ordinary differential inclusions (34A60) Discontinuous ordinary differential equations (34A36) Rate of convergence, degree of approximation (41A25) Attractors of solutions to ordinary differential equations (34D45) Ordinary lattice differential equations (34A33)

Related Items (2)

Stochastic energy balance climate models with Legendre weighted diffusion and an additive cylindrical Wiener process forcing ⋮ Attractors of deterministic and random lattice difference equations

Cites Work

• Unnamed Item
• On the Banach-Saks property
• Dynamics of pattern formation in lateral-inhibition type neural fields
• The inflation of attractors and their discretization: The autonomous case
• Sigmoidal approximations of Heaviside functions in neural lattice models

This page was built for publication: Corrigendum to: ``Sigmoidal approximations of Heaviside functions in neural lattice models