Since World War II, a group of scientists has been attempting to understand the human nervous system and to build computer systems that emulate the brain's abilities. In this collection of interviews, those who helped to shape the field share their childhood memories, their influences, how they became interested in neural networks, and how they envision its future.
Prominent in these recollections are Norbert Wiener, Warren McCulloch, Frank Rosenblatt, and other mythic figures responsible for laying the foundations of modern brain theory and cybernetics. The interviewees agree about some things and disagree about more. Together, they tell the story of how science is actually done, including the false starts and the struggle for jobs, resources, and reputation. Although some of the interviews contain technical material, there is no actual mathematics in the book.
In bringing together seminal articles on the foundations of research, the first volume of Neurocomputing has become an established guide to the background of concepts employed in this burgeoning field. Neurocomputing 2 collects forty-one articles covering network architecture, neurobiological computation, statistics and pattern classification, and problems and applications that suggest important directions for the evolution of neurocomputing.
Researchers will find Neurocomputing an essential guide to the concepts employed in this field that have been taken from disciplines as varied as neuroscience, psychology, cognitive science, engineering, and physics. A number of these important historical papers contain ideas that have not yet been fully exploited, while the more recent articles define the current direction of neurocomputing and point to future research. Each article has an introduction that places it in historical and intellectual perspective.
Included among the 43 articles are the pioneering contributions of McCulloch and Pitts, Hebb, and Lashley; innovative work by Von Neumann, Minsky and Papert, Cooper, Grossberg, and Kohonen; exciting new developments in parallel distributed processing.