Robust Stability Analysis of a Class of Noise Perturbed Two -Time Scale Neural Networks

We establish robustness stability results for uncertain two-time scale neural networks 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 an upper bound for the fast time scale associated with the neural activity state.