Neural networks consist of simple elements capable of summation and thresholding. The authors define a more general element, the task-oriented neuron or teuron, which can compute higher-order functions. They further define teuron networks and show two such networks that can be used as content addressable memories and as pattern classifiers. The first network is based on the Godel encoding scheme. It uses this scheme in order to memorize sequences of numbers. These sequences are then stored analogically. The second network uses binary encoding, i.e., a binary sequence is translated into its decimal equivalent and then stored analogically. The authors demonstrate the feasibility of implementing such networks. It is concluded that they can be implemented in such a way as to reduce cost, due to a reduction in the number of elements coupled with constancy of link values (synaptic weights).