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The paper proposes new neuron architecture for Neural Network models with an aggregation function based on geometric mean of all inputs. This new neuron model gives better accuracy compared to Multilayer Perceptron model (MLP) without increasing the number of parameters. Various error measures have been used with this model. The effectiveness of this model with different error measures have been illustrated on various data sets pertaining to classification, prediction and approximations problems.