不同时间尺度竞争神经网络的流不变性

Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290) Pub Date : 2002-08-07 DOI:10.1109/IJCNN.2002.1005586

A. Meyer-Baese

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引用次数: 6

摘要

复杂神经网络的动力学必须包括长期记忆和短期记忆。神经网络的行为以神经活动方程作为快速现象和突触修饰方程作为神经系统的缓慢部分为特征。本文提出了一种基于流动不变性理论的不同时间尺度系统动力学分析方法。我们能够证明这种系统的解是有界的条件比用k -单调理论时约束更小。

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Flow invariance for competitive neural networks with different time-scales

The dynamics of complex neural networks must include the aspects of long and short-term memory. The behaviour of the network is 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 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.

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Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290)

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