James A. Anderson

James A. Anderson is Professor in the Department of Cognitive and Linguistic Sciences at Brown University.

  • Talking Nets

    Talking Nets

    An Oral History of Neural Networks

    James A. Anderson and Edward Rosenfeld

    Surprising tales from the scientists who first learned how to use computers to understand the workings of the human brain.

    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. Many of the early workers in this field of neural networks came from cybernetics; others came from neuroscience, physics, electrical engineering, mathematics, psychology, even economics. 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 what they see as its future.

    The subjects tell stories that have been told, referred to, whispered about, and imagined throughout the history of the field. Together, the interviews form a Rashomon-like web of reality. Some of the mythic people responsible for the foundations of modern brain theory and cybernetics, such as Norbert Wiener, Warren McCulloch, and Frank Rosenblatt, appear prominently in the recollections. 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 Darwinian struggle for jobs, resources, and reputation. Although some of the interviews contain technical material, there is no actual mathematics in the book.

    ContributorsJames A. Anderson, Michael Arbib, Gail Carpenter, Leon Cooper, Jack Cowan, Walter Freeman, Stephen Grossberg, Robert Hecht-Neilsen, Geoffrey Hinton, Teuvo Kohonen, Bart Kosko, Jerome Lettvin, Carver Mead, David Rumelhart, Terry Sejnowski, Paul Werbos, Bernard Widrow

    • Hardcover $78.00
    • Paperback $40.00
  • An Introduction to Neural Networks

    An Introduction to Neural Networks

    James A. Anderson

    An Introduction to Neural Networks falls into a new ecological niche for texts. Based on notes that have been class-tested for more than a decade, it is aimed at cognitive science and neuroscience students who need to understand brain function in terms of computational modeling, and at engineers who want to go beyond formal algorithms to applications and computing strategies. It is the only current text to approach networks from a broad neuroscience and cognitive science perspective, with an emphasis on the biology and psychology behind the assumptions of the models, as well as on what the models might be used for. It describes the mathematical and computational tools needed and provides an account of the author's own ideas.

    Students learn how to teach arithmetic to a neural network and get a short course on linear associative memory and adaptive maps. They are introduced to the author's brain-state-in-a-box (BSB) model and are provided with some of the neurobiological background necessary for a firm grasp of the general subject.

    The field now known as neural networks has split in recent years into two major groups, mirrored in the texts that are currently available: the engineers who are primarily interested in practical applications of the new adaptive, parallel computing technology, and the cognitive scientists and neuroscientists who are interested in scientific applications. As the gap between these two groups widens, Anderson notes that the academics have tended to drift off into irrelevant, often excessively abstract research while the engineers have lost contact with the source of ideas in the field. Neuroscience, he points out, provides a rich and valuable source of ideas about data representation and setting up the data representation is the major part of neural network programming. Both cognitive science and neuroscience give insights into how this can be done effectively: cognitive science suggests what to compute and neuroscience suggests how to compute it.

    • Hardcover $130.00
    • Paperback $75.00
  • Neurocomputing 2, Volume 2

    Neurocomputing 2, Volume 2

    Directions for Research

    James A. Anderson, Andras Pellionisz, and Edward Rosenfeld

    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.

    • Hardcover $75.00
    • Paperback $70.00
  • Neurocomputing, Volume 1

    Neurocomputing, Volume 1

    Foundations of Research

    James A. Anderson and Edward Rosenfeld

    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.

    • Hardcover $85.00
    • Paperback $90.00

Contributor

  • An Invitation to Cognitive Science, Second Edition, Volume 4

    An Invitation to Cognitive Science, Second Edition, Volume 4

    Methods, Models, and Conceptual Issues

    Daniel N. Osherson, Saul Sternberg, and Don Scarborough

    The chapters in this volume span many areas of cognitive science—including artificial intelligence, neural network models, animal cognition, signal detection theory, computational models, reaction-time methods, and cognitive neuroscience.

    An Invitation to Cognitive Science provides a point of entry into the vast realm of cognitive science by treating in depth examples of issues and theories from many subfields. The first three volumes of the series cover Language, Visual Cognition, and Thinking.

    Volume 4, Methods, Models, and Conceptual Issues, expands the series in new directions. The chapters span many areas of cognitive science—including artificial intelligence, neural network models, animal cognition, signal detection theory, computational models, reaction-time methods, and cognitive neuroscience. The volume also offers introductions to several general methods and theoretical approaches for analyzing the mind, and shows how some of these approaches are applied in the development of quantitative models.

    Rather than general and inevitably superficial surveys of areas, the contributors present "case studies"—detailed accounts of one or two achievements within an area. The goal is to tell a good story, challenging the reader to embark on an intellectual adventure.

    • Hardcover $23.75
    • Paperback $10.75