What would it mean for the semantic content of an utterance to be represented in a neural network, enabling, for example, inferential question-answering? The anti-representationalist (or “eliminativist”) view would be that no particular structures can be or need to be identified as encoding semantic content. The input modifies the activity of the network and the strengths of various connections in a distributed way, such that the subsequent behavior of the network effectively implements inferential question-answering. However, this leaves entirely open how a network would learn this sort of behavior. The most successful neural net experiments have been aimed at mapping input patterns to class labels or to other very restricted sets of outputs, and they have required numerous labeled examples (e.g., thousands of images labeled with the class of the objects depicted) to learn their task. By contrast, humans excel at “one-shot” learning, and can perform complex tasks based on such learning. 