title: On the stability analysis of delayed neural networks systems.
abstract: In this paper, the problems of stability of delayed neural networks are investigated, including the stability of discrete and distributed delayed neural networks. Under the generalization of dropping the Lipschitzian hypotheses for output functions, some stability criteria are obtained by using the Liapunov functional method. We do not assume the symmetry of the connection matrix and we establish that the system admits a unique equilibrium point in which the output functions do not satisfy the Lipschitz conditions and do not require them to be differential or strictly monotonously increasing. These criteria can be used to analyze the dynamics of biological neural systems or to design globally stable artificial neural networks.
authors.affiliation: Laboratoire Scribens, Ecole Polytechnique de Montréal, Québec, Canada. feng@scribens.polymtl.ca
authors.forename: C
authors.initials: C
authors.lastname: Feng
authors.forename: R
authors.initials: R
authors.lastname: Plamondon
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000465:Algorithms; D000818:Animals; D002490:Central Nervous System / Q000502:physiology*; D005246:Feedback / Q000502:physiology; D006801:Humans; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D009435:Synaptic Transmission / Q000502:physiology