“The Utility Driven Dynamic Error Propagation Network (RTRL)”, 1987-11-04 (; backlinks):
[later: 1995] Error propagation networks are able to learn a variety of tasks in which a static input pattern is mapped onto a static output pattern. This paper presents a generalisation of these nets to deal with time varying, or dynamic, patterns. Three possible architectures are explored which deal with learning sequences of known finite length and sequences of unknown and possibly infinite length. Several examples are given and an application to speech coding is discussed.
A further development of dynamic nets is made which allows them to be trained by a signal which expresses the correctness of the output of the net, the utility signal. On one possible architecture for such utility driven dynamic nets is given and a simple example is presented. Utility driven dynamic nets are potentially able to calculate and maximize any function of the input and output data streams, within the considered context. This is a very powerful property, and an appendix presents a comparison of the information processing in utility driven dynamic nets and that in the human brain.