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.
Neural Networks and Animal Behavior 1st Edition by Magnus Enquist, Stefano Ghirlanda ISBN 0691096333 9780691096339
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Product details:
ISBN 10: 0691096333
ISBN 13: 9780691096339
Author: Magnus Enquist, Stefano Ghirlanda
Neural Networks and Animal Behavior 1st Table of contents:
Chapter 1. Understanding Animal Behavior
1.1 The causes of behavior
1.2 A framework for models of behavior
1.3 The structure of behavior models
1.4 Neural network models
Chapter 2. Fundamentals of Neural Network Models
2.1 Network nodes
2.2 Network architectures
2.3 Achieving specific input-output mappings
2.4 Organizing networks without specific guidance
2.5 Working with your own models
Chapter 3. Mechanisms of Behavior
3.1 Analysis of behavior systems
3.2 Building neural network models
3.3 Reactions to stimuli
3.4 Sensory processing
3.5 Temporal patterns
3.6 Many sources of information and messy information
3.7 Central mechanisms of decision making
3.8 Motor control
3.9 Consequences of damage to nervous systems
Chapter 4. Learning and Ontogeny
4.1 What are learning and ontogeny?
4.2 General aspects of learning
4.3 Network models of general learning phenomena
4.4 Behaviorally silent learning
4.5 Comparison with animal learning theory
4.6 Training animals versus training networks
4.7 Ontogeny
4.8 Conclusions
Chapter 5. Evolution
5.1 The evolution of behavior systems
5.2 Requirements for evolving behavior mechanisms
5.3 The material basis of behavioral evolution
5.4 Exploring evolution with neural network models
5.5 Conclusions
Chapter 6. Conclusions
6.1 Are neural networks good models of behavior?
6.2 Do we use too simple network models?
6.3 Comparisons with other models
6.4 Neural networks and animal cognition
6.5 Final words
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