Written for cognitive scientists, psychologists, computer scientists, engineers, and neuroscientists, this book provides an accessible overview of how computational network models are being used to model neurobiological phenomena. Each chapter presents a representative example of how biological data and network models interact with the authors' research. The biological phenomena cover network- or circuit-level phenomena in humans and other higher-order vertebrates.

Composed of three sections, this book presents the training algorithm for neural networks: backpropagation. The first presents the theory and principles; the second a number of network architectures; and the third shows how these can be applied to a number of different fields.

Composed of three sections, this book presents the training algorithm for neural networks: backpropagation. The first presents the theory and principles; the second a number of network architectures; and the third shows how these can be applied to a number of different fields.

The philosophy of cognitive science has recently become one of the most exciting and fastest growing domains of philosophical inquiry and analysis. Until the early 1980s, nearly all of the models developed treated cognitive processes -- like problem solving, language comprehension, memory, and higher visual processing -- as rule-governed symbol manipulation. However, this situation has changed dramatically over the last half dozen years. In that period there has been an enormous shift of attention toward connectionist models of cognition that are inspired by the network-like architecture of...