PATTERN FORMATION IN REACTION-DIFFUSION NEURAL NETWORKS WITH LEAKAGE DELAY

被引:7
作者
Lin, Jiazhe [1 ]
Xu, Rui [2 ]
Tian, Xiaohong [2 ]
机构
[1] Army Engn Univ, Inst Appl Math, Shijiazhuang 050003, Hebei, Peoples R China
[2] Shanxi Univ, Complex Syst Res Ctr, Taiyuan 030006, Shanxi, Peoples R China
来源
JOURNAL OF APPLIED ANALYSIS AND COMPUTATION | 2019年 / 9卷 / 06期
基金
中国国家自然科学基金;
关键词
Pattern formation; reaction-diffusion; leakage delay; neural network; amplitude equation; HOPF-BIFURCATION ANALYSIS; ASSOCIATIVE MEMORY; 2-NEURON NETWORK; TIME-DELAY; DYNAMICS; MODEL;
D O I
10.11948/20190001
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
Due to the heterogeneity of the electromagnetic field in neural networks, the diffusion phenomenon of electrons exists inevitably. In this paper, we investigate pattern formation in a reaction-diffusion neural network with leakage delay. The existence of Hopf bifurcation, as well as the necessary and sufficient conditions for Turing instability, are studied by analyzing the corresponding characteristic equation. Based on the multiple-scale analysis, amplitude equations of the model are derived, which determine the selection and competition of Turing patterns. Numerical simulations are carried out to show the possible patterns and how these patterns evolve. In some cases, the stability performance of Turing patterns is weakened by leakage delay and synaptic transmission delay.
引用
收藏
页码:2224 / 2244
页数:21
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