Publisher's Synopsis
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.
