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Flow invariance for competitive neural networks with different time-scales

被引：6
|
作者：
Meyer-Bäse, A [1 ]
机构：
[1] Florida State Univ, Anke Meyer Base Dept Elect & Comp Engn, Tallahassee, FL 32310 USA
来源：
PROCEEDING OF THE 2002 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-3 | 2002年
关键词：
D O I：
10.1109/IJCNN.2002.1005586
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The dynamics of complex neural networks must include the aspects of long and short-term memory. The behaviour of the network is such characterized by an equation of neural activity as a fast phenomenon and an equation of synaptic modification as a slow part of the neural system. We present a new method of analyzing the dynamics of a system with different time scales based on the theory of flow invariance. We are able to show the conditions under which the solutions of such a system are bounded being less restrictive than with the K-monotone theory [LK89].
引用
收藏
页码：858 / 861
页数：2
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