Abstract: A Generalized Shunting Inhibition Neuron (GSIN) model is proposed by analyzing shortcomings of the normal shunting neuron model. A new feed forward neural network architecture based on GSIN, naming Generalized Shunting Inhibition Neural Network (GSINN), and its learning algorithm are then introduced. Finally, the GSINN is applied to several benchmark classification problems, and their performance is compared with the performances of Shunting Inhibitory Artificial Neural Network (SIANN) and BP networks, and the effectiveness of the proposed network structure and learning algorithm is verified. Experimental results show that a single GSIN and simple GSINN can outperform both the SIANN and Back Propagation (BP) network.