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Neural networks began to be the object of serious research in the 1940s. They did not become widely known or popular until the 1980s when several academic centers published articles and distributed software that enabled individuals with modest mathematical and computing skills to learn computational network principles and apply them to broad ranges of projects inside university settings as well as in extramural domains. At first, neural networks were predominantly applied to industrial problems in areas such as pattern recognition and signal processing. However, many active neural network researchers were guided by insights from neuroscience and psychology, and believed that neural networks could become an important computational technique for furthering our understanding of brain and cognitive processes. Prerequisites of a technical nature are minimized in this book. The mathematics involved in the network models are deemphasized, and in most cases the theoretical basis for the model's structure can be discerned from the diagrams combined with the text. Hence, the aim is to present neural network techniques as theoretical tools that can be readily learned and applied by neuropsychologists and other neuroscientists (including neurologists, psychiatrists, clinical psychologists, mathematicians, and computer scientists) with particular interest in clinical patient applications.
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