In this paper we show that the mathematical theory known as image algebra not only incorporates the mathematics underlying artificial neural networks, but also provides for novel methods of neural computing. These methods are not covered by current neural network models but are an intrinsic part of the image algebra. In this sense, image algebra provides a mathematical framework for a more general theory of artificial neural networks and a language for computing with neural networks.