Abstract
The paper proposes a novel neuron model termed as Generalized Power Mean Neuron model (GPMN). The paper focuses on illustrating the computational power and the generalization capability of this model. In this model, the aggregation function is based on generalized power mean of the inputs. The performance of the neural network using GPMN model is compared with traditional feed-forward neural network on several benchmark classification problems. It has been shown that the neural network using GPMN model performs far superior compared to the traditional feed-forward neural network both in terms of accuracy and speed.