Quadratic-Type Lyapunov Functions for Competitive Neural Networks with Different Time-Scales
Abstract
The dynamics of complex neural networks modelling the self(cid:173) organization process in cortical maps 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 phenom(cid:173) enon and an equation of synaptic modification as a slow part of the neural system. We present a quadratic-type Lyapunov function for the flow of a competitive neural system with fast and slow dynamic variables. We also show the consequences of the stability analysis on the neural net parameters.
Cite
Text
Meyer-Bäse. "Quadratic-Type Lyapunov Functions for Competitive Neural Networks with Different Time-Scales." Neural Information Processing Systems, 1995.Markdown
[Meyer-Bäse. "Quadratic-Type Lyapunov Functions for Competitive Neural Networks with Different Time-Scales." Neural Information Processing Systems, 1995.](https://mlanthology.org/neurips/1995/meyerbase1995neurips-quadratictype/)BibTeX
@inproceedings{meyerbase1995neurips-quadratictype,
title = {{Quadratic-Type Lyapunov Functions for Competitive Neural Networks with Different Time-Scales}},
author = {Meyer-Bäse, Anke},
booktitle = {Neural Information Processing Systems},
year = {1995},
pages = {337-343},
url = {https://mlanthology.org/neurips/1995/meyerbase1995neurips-quadratictype/}
}