There is a sense among scientists that the time is finally ripe for the problem of consciousness to be solved once and for all. The development of new experimental and theoretical tools for probing the brain has produced an atmosphere of unparalleled optimism that the job can now be done properly: The race for consciousness is on!
It is now clear that the brain is unlikely to be understood without recourse to computational theories. The theme of An Introduction to Natural Computation is that ideas from diverse areas such as neuroscience, information theory, and optimization theory have recently been extended in ways that make them useful for describing the brain's programs. This book provides a comprehensive introduction to the computational material that forms the underpinnings of the currently evolving set of brain models.
Over the past few years, computer modeling has become more prevalent in the clinical sciences as an alternative to traditional symbol-processing models. This book provides an introduction to the neural network modeling of complex cognitive and neuropsychological processes. It is intended to make the neural network approach accessible to practicing neuropsychologists, psychologists, neurologists, and psychiatrists. It will also be a useful resource for computer scientists, mathematicians, and interdisciplinary cognitive neuroscientists.
Most practical applications of artificial neural networks are based on a computational model involving the propagation of continuous variables from one processing unit to the next. In recent years, data from neurobiological experiments have made it increasingly clear that biological neural networks, which communicate through pulses, use the timing of the pulses to transmit information and perform computation.
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.