2020
DOI: 10.3934/dcdsb.2019268
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Attractors of Hopfield-type lattice models with increasing neuronal input

Abstract: Dedicated to Juan J. Nieto on the occasion of his 60th birthday Abstract. Two Hopfield-type neural lattice models are considered, one with local n-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 n → ∞.

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Cited by 10 publications
(3 citation statements)
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“…Recent studies on X. Wang et al NoDEA neural lattice models include the traveling fronts for a class of lattice neural field equations by Faye [16], long term dynamics for neural field lattice models by Han and Kloeden [21,22], Wang et al [36,38], long term dynamics for Hopfiled-type neural lattice models by Han et al [23,24], Wang et al [35]. Most recently, Sui et al studied the existence and structure of random attractors for a neural lattice model with delays [29].…”
Section: Introductionmentioning
confidence: 99%
“…Recent studies on X. Wang et al NoDEA neural lattice models include the traveling fronts for a class of lattice neural field equations by Faye [16], long term dynamics for neural field lattice models by Han and Kloeden [21,22], Wang et al [36,38], long term dynamics for Hopfiled-type neural lattice models by Han et al [23,24], Wang et al [35]. Most recently, Sui et al studied the existence and structure of random attractors for a neural lattice model with delays [29].…”
Section: Introductionmentioning
confidence: 99%
“…Up to now, long-range interaction has been studied for different physical systems. For examples, traveling wave solution for lattice model with infinite-range interaction can be found in [3,36], the continuum limit for discrete nonlinear Schrödinger equations with long-range lattice interactions are considered in [26,27], attractors of neural lattice model are studied in [23,24,34] for finite-range interaction and [22,39] for infinite-range interaction.…”
mentioning
confidence: 99%
“…It is worth mentioning that the fractional discrete Laplacian (−∆ d ) s is nonlocal and hence deriving uniform estimates on the tails of solutions are much more involved than the discrete Laplacian −∆ d . In addition, lattice with fractional discrete Laplacian is different from the neural networks lattice with long-range interaction that are considered in [22,23,24,34,39].…”
mentioning
confidence: 99%