Stability constraints for oscillatory neural networks

The stability of the equilibrium point (background activity) of oscillatory neural networks is an important property for computational applications th at explore the switching between background activity and oscillatory states . Here we consider a general approach to this problem for networks of arbit rary size. For symmetric coupling, often the case in associative learning a lgorithms, we derive the stability constraints and establish explicit resul ts for the coupling strengths to satisfy in order that the equilibrium stat e is stable. (C) 2000 Elsevier Science B.V. All rights reserved.