Magnus Enquist

Neural Networks and Animal Behavior (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

Produktdetails

• Verlag: Princeton University Press
• Seitenzahl: 256
• Erscheinungstermin: 3. Dezember 2013
• Englisch
• ISBN-13: 9781400850785
• Artikelnr.: 40056302

Autorenporträt

Magnus Enquist & Stefano Ghirlanda

Inhaltsangabe

Preface vii
Chapter 1. Understanding Animal Behavior 1
1.1 The causes of behavior 2
1.2 A framework for models of behavior 4
1.3 The structure of behavior models 7
1.4 Neural network models 18
Chapter 2. Fundamentals of Neural Network Models 31
2.1 Network nodes 31
2.2 Network architectures 39
2.3 Achieving specific input-output mappings 45
2.4 Organizing networks without specific guidance 57
2.5 Working with your own models 58
Chapter 3. Mechanisms of Behavior 67
3.1 Analysis of behavior systems 67
3.2 Building neural network models 70
3.3 Reactions to stimuli 75
3.4 Sensory processing 89
3.5 Temporal patterns 96
3.6 Many sources of information and messy information 99
3.7 Central mechanisms of decision making 100
3.8 Motor control 115
3.9 Consequences of damage to nervous systems 123
Chapter 4. Learning and Ontogeny 129
4.1 What are learning and ontogeny? 129
4.2 General aspects of learning 130
4.3 Network models of general learning phenomena 141
4.4 Behaviorally silent learning 151
4.5 Comparison with animal learning theory 155
4.6 Training animals versus training networks 159
4.7 Ontogeny 160
4.8 Conclusions 170
Chapter 5. Evolution 173
5.1 The evolution of behavior systems 173
5.2 Requirements for evolving behavior mechanisms 175
5.3 The material basis of behavioral evolution 178
5.4 Exploring evolution with neural network models 186
5.5 Conclusions 202
Chapter 6. Conclusions 205
6.1 Are neural networks good models of behavior? 205
6.2 Do we use too simple network models? 208
6.3 Comparisons with other models 208
6.4 Neural networks and animal cognition 210
6.5 Final words 218
Bibliography 219
Index 249