Robust stability analysis of competitive neural networks with different time-scales under perturbations

We establish robust stability results for competitive neural networks with different time-scales under parameter perturbations and determine conditions that ensure the existence of asymptotically stable equilibria of the perturbed neural system. It is assumed that the system uncertainties are limited by the upper bounds of their norms. We derive a Lyapunov function for the coupled system and a maximal upper bound for the fast time-scale associated with the neural activity state.