Neural Networks and Animal Behavior / / Stefano Ghirlanda, Magnus Enquist.

• Superior document: Title is part of eBook package: De Gruyter Princeton University Press eBook-Package Backlist 2000-2013
• VerfasserIn: Enquist, Magnus,; Ghirlanda, Stefano,
• Place / Publishing House: Princeton, NJ : : Princeton University Press, , [2013]; ©2006
• Year of Publication: 2013
• Edition: Course Book
• Language: English
• Series: Monographs in Behavior and Ecology ; 33
• Physical Description: 1 online resource (256 p.) :; 12 halftones. 102 line illus. 6 tables.
• Other title: Frontmatter --; Contents --; Preface --; Chapter One. Understanding Animal Behavior --; Chapter Two. Fundamentals of Neural Network Models --; Chapter Three. Mechanisms of Behavior --; Chapter Four. Learning and Ontogeny --; Chapter Five. Evolution --; Chapter Six. Conclusions --; Bibliography --; Index
• Format: Mode of access: Internet via World Wide Web.
• ISBN: 9781400850785; 9783110442502
• DOI: 10.1515/9781400850785
• Access: restricted access
• Hierarchical level: Monograph
• Statement of Responsibility: Stefano Ghirlanda, Magnus Enquist.

How can we make better sense of animal behavior by using what we know about the brain? This is the first book that attempts to answer this important question by applying neural network theory. Scientists create Artificial Neural Networks (ANNs) to make models of the brain. These networks mimic the architecture of a nervous system by connecting elementary neuron-like units into networks in which they stimulate or inhibit each other's activity in much the same way neurons do. This book shows how scientists can employ ANNs to analyze animal behavior, explore the general principles of the nervous systems, and test potential generalizations among species. The authors focus on simple neural networks to show how ANNs can be investigated by math and by computers. They demonstrate intuitive concepts that make the operation of neural networks more accessible to nonspecialists. The first chapter introduces various approaches to animal behavior and provides an informal introduction to neural networks, their history, and their potential advantages. The second chapter reviews artificial neural networks, including biological foundations, techniques, and applications. The following three chapters apply neural networks to such topics as learning and development, classical instrumental condition, and the role of genes in building brain networks. The book concludes by comparing neural networks to other approaches. It will appeal to students of animal behavior in many disciplines. It will also interest neurobiologists, cognitive scientists, and those from other fields who wish to learn more about animal behavior.