New single neuron structure for solving nonlinear problems

Feedforward multilayer neural networks are widely used for pattern recognition in diverse fields of applications. However, their inherent structural element, the perceptron, cannot perform pattern classification on nonlinearly separable patterns. These severe limitations motivated us in investigating the validity of a new structure for a single neuron capable of recognizing nonlinear patterns such as the XOR problem. This new architecture is inspired by biological assumptions involving stochastic processes. It is clearly established that only six-parameters are necessary to solve the XOR problem. Higher order problems are also investigated