Stabilization role of inhibitory self-connections in a delayed neural network

被引：62
作者：
van den Driessche, P
Wu, JH
Zou, XF [1 ]
机构：
[1] Univ Victoria, Dept Math & Stat, Victoria, BC V8W 3P4, Canada
[2] York Univ, Dept Math & Stat, Toronto, ON M3J 1PE, Canada
[3] Mem Univ Newfoundland, Dept Math & Stat, St John, NF A1C 5S7, Canada
来源：
PHYSICA D-NONLINEAR PHENOMENA | 2001年 / 150卷 / 1-2期
基金：
加拿大自然科学与工程研究理事会;
关键词：
neural networks; time delay; stability; monotone dynamical systems; embedding; matrix measure;
D O I：
10.1016/S0167-2789(00)00216-5
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In a delayed Hopfield neural network that is strongly connected with non-inhibitory interconnections, fast and inhibitory self-connections lead to global convergence to a unique equilibrium of the network. By applying monotone dynamical systems theory and an embedding technique, we prove that this conclusion remains true without the requirement of strong connectivity or non-inhibitory interconnections. (C) 2001 Elsevier Science B.V, All rights reserved.
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页码：84 / 90
页数：7
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