Memory, Attention, and Decision-Making: A unifying computational neuroscience approach / Edition 1

by Edmund Rolls

Overview

Memory, attention, and decision-making are three major areas of cognitive neuroscience. They are, however, frequently studied in isolation, using a range of models to understand them. This book brings a unified approach to understanding these three processes. It shows how these fundamental functions for cognitive neuroscience can be understood in a common and unifying computational neuroscience framework. This framework links empirical research on brain function from neurophysiology, function neuroimaging, and the effects of brain damage, to a description of how neural networks in the brain implement these functions using a set of common principles. The book describes the principles of operation of these networks, and how they could implement such important functions as memory, attention, and decision-making.

Product Details

ISBN-13:	9780199232703
Publisher:	Oxford University Press, USA
Publication date:	08/16/2007
Pages:	820
Product dimensions:	9.70(w) x 6.60(h) x 1.70(d)

Editorial Reviews

Reviewer: Christopher J Graver, PhD, ABPP-CN(Madigan Healthcare System)
Description: Understanding the brain processes of memory, attention, and decision-making requires integrated study from the cellular to the computational level. This book explores these brain processes from multiple perspectives to provide a better understanding of brain function.
Purpose: Using research from neuropsychology, neurophysiology, and neuroimaging, this book aims to link clinical information with testable neural computational models.
Audience: The intended audience is primarily students and researchers in the areas of computational and cognitive neuroscience, as well as neurophysiology and neuropsychology. The editor has a long and prolific career in computational neuroscience.
Features: This is a very comprehensive book with detailed coverage of the major conceptual areas of memory, attention, and decision-making. Within these major areas, the chapters are broken down into very fine explorations. For example, the chapter on reward- and punishment-related learning looks at numerous possible brain areas and networks that include the orbitofrontal cortex, amygdala, and cingulated cortex. It also further investigates this concept in terms of other factors, such as emotional state and exogenous influences. The numerous studies described in this text are accompanied by figures, illustrations, and tables. These are very helpful for explaining more advanced or technical concepts. Whereas some chapters are very mathematically oriented, others have quite an array of clinical information. Furthermore, for readers in need of computational neuroscience grounding before launching into such a book, there are two rather complete appendixes that are quite helpful. The table of contents and index are helpful for quick referencing and the references are up to date.
Assessment: The main strength of this book, compared to similar computational neuroscience books, is the functional, practical, and clinical links that are made to theoretical and computational models. The overall significance and contribution of computational neuroscience is quite apparent and the marriage of these models with neuropsychology and neuroimaging is well conceived.

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