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Flow invariance for competitive neural networks with different time-scales

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1 Author(s)
Meyer-Baese, A. ; Dept. of Electr. & Comput. Eng., Florida State Univ., Tallahassee, FL

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

Published in:

Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on  (Volume:1 )

Date of Conference:

2002