Attractors of Hopfield-type lattice models with increasing neuronal input
Xiaoli Wang, Peter E. Kloeden, Xiaoying Han · Discrete and Continuous Dynamical Systems - B · 2019
Two Hopfield-type neural lattice models are considered, one with local \begin{document}$ n $\end{document} -neighborhood nonlinear interconnections among neurons and the other with global nonlinear interconnections among neurons. It is shown that both systems possess global attractors on a weighted space of bi-infinite sequences. Moreover, the attractors are shown to depend upper semi-continuously on the interconnection parameters as \begin{document}$ n \to \infty $\end{document} .