Skip to Main Content
Have library access? Log in through your library
Neural Networks and Animal Behavior:

Neural Networks and Animal Behavior:

Magnus Enquist
Stefano Ghirlanda
Copyright Date: 2005
Edition: STU - Student edition
Pages: 272
  • Cite this Item
  • Book Info
    Neural Networks and Animal Behavior:
    Book Description:

    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.

    eISBN: 978-1-4008-5078-5
    Subjects: Zoology, Technology

Table of Contents

  1. Front Matter
    (pp. i-iv)
  2. Table of Contents
    (pp. v-vi)
  3. Preface
    (pp. vii-xii)
  4. Chapter One Understanding Animal Behavior
    (pp. 1-30)

    The subject of this book is animal behavior. What is animal behavior, and what does it mean to understand animal behavior? As we shall see in this chapter, there are several answers to these questions, depending on research tradition and what one intends to understand. At the same time, different theories of behavior have much the same scope:

    They deal with how the animal as a whole interacts with its physical, ecological and social environment, in particular through reception of sensory stimulation and behavioral actions such as motor patterns, pheromone release, change in body coloration and so forth.

    They want...

  5. Chapter Two Fundamentals of Neural Network Models
    (pp. 31-66)

    This chapter contains a technical presentation of neural network models and of how they are rooted in neurophysiology. The aim is to provide the reader with a basic understanding of how neural network models work and how to build them. We begin with basic concepts of neurophysiology, which are then summarized in a simple mathematical model of a network node. We continue showing different ways to connect nodes into networks and discuss what different networks can do. Then we consider how to set network connections so that the network behaves as desired. We conclude with a section on computer simulation...

  6. Chapter Three Mechanisms of Behavior
    (pp. 67-128)

    This chapter presents neural network models of short term and reversible changes in behavior (traditionally referred to asmotivation). The topics covered include reactions to stimuli, making decisions and the control of movement. In our terminology, these are properties of a given behavior map (Chapter 1).Learningandontogeny(development), in contrast, correspond to changes in a behavior map and are considered in Chapter 4.

    Before considering neural network models, we discuss in some detail the nature of motivational variables, how they cause behavior and how they enter models of behavior systems. The aim is to sketch a conceptual frame...

  7. Chapter Four Learning and Ontogeny
    (pp. 129-172)

    This chapter deals with the ontogeny, or development, of behavior. Many factors and processes interact in behavioral development. Particularly important are those that create the nervous system and those that govern learning. The latter are here given special attention owing to their importance for behavior. We begin by discussing what learning and ontogeny are and how they relate to each other, to behavior and to the nervous system. We continue with a detailed section on neural network models of basic learning processes, such as classical conditioning. Learning is an extensively developed topic that we cannot cover in full. Our only...

  8. Chapter Five Evolution
    (pp. 173-204)

    In this chapter we tackle the evolution of behavior and nervous systems. All multicellular animals, with a few exceptions, have a nervous system. Phylogenetic studies suggest a single origin, which consequently must date back more than 500 million years. The nervous system offers an all-purpose machinery for receiving information and producing responses, allowing the organism to be much more flexible toward its environment. With a nervous system, organisms can move, perform all sorts of activities, communicate, generate internal responses such as heartbeat, gut movements and hormone secretions, and so on. Other solutions for doing some of these things exist, e.g.,...

  9. Chapter Six Conclusions
    (pp. 205-218)

    In this book we have explored the potential of neural networks to model behavior. The results suggest that neural networks can model behavior systems in all their parts, reproducing a wide range of behavioral phenomena. Figure 6.1 provides a summarizing sketch of a simple but complete model of an animal’s behavior mechanism. The model includes reception and further processing of sensory input, central mechanisms of decision making and the control of muscles and other effectors. The figure shows only a few recurrent connections, but in principle connections between any nodes in the system can be included, e.g., so that sensory...

  10. Bibliography
    (pp. 219-248)
  11. Index
    (pp. 249-253)