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In this article, we propose a new class of artificial neural networks for classification and function approximation. These networks are referred to as shunting inhibitory artificial neural networks (SIANNs). A SIANN consists of one or more hidden layers comprised of shunting neurons, the outputs of which are combined linearly to form the desired output. The basic synaptic interaction of the hidden units is shunting inhibition. Due to the inherent non-linearity mediated by shunting inhibition, SIAN networks are capable of constructing a large repertoire of decision surfaces, ranging from simple hyper-planes to very complex nonlinear hyper-surfaces. Therefore, developing efficient training algorithms for these networks should simplify the design of very powerful classifiers and function approximators. In this paper some examples of complex decision regions formed by SIANNs are illustrated. Furthermore, a method for training feedforward SIANNs is developed based on the error backpropagation algorithm. Finally, simulation results, which illustrate the performance of SIANN in function approximation and classification tasks, are presented and compared with results obtained from multilayer perceptron networks.