Competitive learning´s global search property

The competitively inhibited neural net (CINN) is a special class of competitive learning paradigm which is amenable to formal analysis. The authors present evidence that the CINN is capable of globally optimizing certain problems. They suggest that the CINN is capable of locating the primary mode of a source density function. In the event that this density represents a performance functional (such as in maximum-likelihood estimation), the CINN can be used to locate the global optimum of the performance functional. The mechanisms behind this global search property have been explained using a nonlinear diffusion model of CINN learning, and simulation experiments have corroborated this capability of the CINN for a minimally deceptive problem