Delay learning in artificial neural networks (Chapman & Hall neural computing series)

In this book the author presents a method for implementing delay learning in artificial neural networks. She shows that such a system bears some behavioural (and possibly, at a deeper level, conceptual) similarities to the bilogical methods by which living creatures accomplish the same tasks. The book begins with an overview of the existing neural network paradigms which address some features of delay learning. The attention-driven buffering approach is described; this allows a system of finite size to learn about action-reinforcement associations, even in situations where reinforcements are delayed indefinitely and may be interleaved with others. This approach is shown to be applicable to an operant conditioning task - which is also an example of delay learning in animals. the biological relevance of the attention-driving buffering approach is discussed in later chapters where it is compared in detail with the learning system in one specific animal - the octopus - and with higher animals such as mammals.