Abstract
This contribution presents a new method of analyzing the dynamics of a biological relevant neural network with different time scales based on the theory of flow invariance. We are able to show that the resulting stability conditions are less restrictive and more general than with K-monotone theory or singular perturbation theory. The theoretical results are further substantiated by simulation results conducted for analysis and design of these neural networks.
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