Flow invariance for competitive neural networks with different time-scales

Author

Meyer-Baese, Anke

Author_Institution

Dept. of Electr. & Comput. Eng., Florida State Univ., Tallahassee, FL

Volume

1

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

858

Lastpage

861

Abstract

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

Keywords

dynamics; invariance; neural nets; unsupervised learning; competitive neural networks; complex neural networks; dynamics; fast phenomenon; flow invariance; long short-term memory; neural activity; short-term memory; synaptic modification; time-scales; Convergence; Differential equations; Hebbian theory; Interference; Large-scale systems; Neural networks; Neurons; Nonlinear dynamical systems; Stability criteria; State-space methods;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on

Conference_Location

Honolulu, HI

ISSN

1098-7576

Print_ISBN

0-7803-7278-6

Type

conf

DOI

10.1109/IJCNN.2002.1005586

Filename

1005586