Daniel S. Levine – författare

This textbook provides a general introduction to the field of neural networks. Thoroughly revised and updated from the previous editions of 1991 and 2000, the current edition concentrates on networks for modeling brain processes involved in cognitive and behavioral functions. Part one explores the philosophy of modeling and the field’s history starting from the mid-1940s, and then discusses past models of associative learning and of short-term memory that provide building blocks for more complex recent models. Part two of the book reviews recent experimental findings in cognitive neuroscience and discusses models of conditioning, categorization, category learning, vision, visual attention, sequence learning, behavioral control, decision making, reasoning, and creativity. The book presents these models both as abstract ideas and through examples and concrete data for specific brain regions.

The book includes two appendices to help ground the reader: one reviewing the mathematics used in network modeling, and a second reviewing basic neuroscience at both the neuron and brain region level. The book also includes equations, practice exercises, and thought experiments.

This textbook provides a general introduction to the field of neural networks. Thoroughly revised and updated from the previous editions of 1991 and 2000, the current edition concentrates on networks for modeling brain processes involved in cognitive and behavioral functions. Part one explores the philosophy of modeling and the field’s history starting from the mid-1940s, and then discusses past models of associative learning and of short-term memory that provide building blocks for more complex recent models. Part two of the book reviews recent experimental findings in cognitive neuroscience and discusses models of conditioning, categorization, category learning, vision, visual attention, sequence learning, behavioral control, decision making, reasoning, and creativity. The book presents these models both as abstract ideas and through examples and concrete data for specific brain regions.

The book includes two appendices to help ground the reader: one reviewing the mathematics used in network modeling, and a second reviewing basic neuroscience at both the neuron and brain region level. The book also includes equations, practice exercises, and thought experiments.

The second published collection based on a conference sponsored by the Metroplex Institute for Neural Dynamics -- the first is Motivation, Emotion, and Goal Direction in Neural Networks (LEA, 1992) -- this book addresses the controversy between symbolicist artificial intelligence and neural network theory. A particular issue is how well neural networks -- well established for statistical pattern matching -- can perform the higher cognitive functions that are more often associated with symbolic approaches. This controversy has a long history, but recently erupted with arguments against the abilities of renewed neural network developments. More broadly than other attempts, the diverse contributions presented here not only address the theory and implementation of artificial neural networks for higher cognitive functions, but also critique the history of assumed epistemologies -- both neural networks and AI -- and include several neurobiological studies of human cognition as a real system to guide the further development of artificial ones. Organized into four major sections, this volume: * outlines the history of the AI/neural network controversy, the strengths and weaknesses of both approaches, and shows the various capabilities such as generalization and discreetness as being along a broad but common continuum; * introduces several explicit, theoretical structures demonstrating the functional equivalences of neurocomputing with the staple objects of computer science and AI, such as sets and graphs; * shows variants on these types of networks that are applied in a variety of spheres, including reasoning from a geographic database, legal decision making, story comprehension, and performing arithmetic operations; * discusses knowledge representation process in living organisms, including evidence from experimental psychology, behavioral neurobiology, and electroencephalographic responses to sensory stimuli.

The second published collection based on a conference sponsored by the Metroplex Institute for Neural Dynamics -- the first is Motivation, Emotion, and Goal Direction in Neural Networks (LEA, 1992) -- this book addresses the controversy between symbolicist artificial intelligence and neural network theory. A particular issue is how well neural networks -- well established for statistical pattern matching -- can perform the higher cognitive functions that are more often associated with symbolic approaches. This controversy has a long history, but recently erupted with arguments against the abilities of renewed neural network developments. More broadly than other attempts, the diverse contributions presented here not only address the theory and implementation of artificial neural networks for higher cognitive functions, but also critique the history of assumed epistemologies -- both neural networks and AI -- and include several neurobiological studies of human cognition as a real system to guide the further development of artificial ones. Organized into four major sections, this volume: * outlines the history of the AI/neural network controversy, the strengths and weaknesses of both approaches, and shows the various capabilities such as generalization and discreetness as being along a broad but common continuum; * introduces several explicit, theoretical structures demonstrating the functional equivalences of neurocomputing with the staple objects of computer science and AI, such as sets and graphs; * shows variants on these types of networks that are applied in a variety of spheres, including reasoning from a geographic database, legal decision making, story comprehension, and performing arithmetic operations; * discusses knowledge representation process in living organisms, including evidence from experimental psychology, behavioral neurobiology, and electroencephalographic responses to sensory stimuli.

This book is the third in a series based on conferences sponsored by the Metroplex Institute for Neural Dynamics, an interdisciplinary organization of neural network professionals in academia and industry. The topics selected are of broad interest to both those interested in designing machines to perform intelligent functions and those interested in studying how these functions are actually performed by living organisms and generate discussion of basic and controversial issues in the study of mind. The topic of optimality was chosen because it has provoked considerable discussion and controversy in many different academic fields. There are several aspects to the issue of optimality. First, is it true that actual behavior and cognitive functions of living animals, including humans, can be considered as optimal in some sense? Second, what is the utility function for biological organisms, if any, and can it be described mathematically? Rather than organize the chapters on a "biological versus artificial" basis or by what stance they took on optimality, it seemed more natural to organize them either by what level of questions they posed or by what intelligent functions they dealt with. The book begins with some general frameworks for discussing optimality, or the lack of it, in biological or artificial systems. The next set of chapters deals with some general mathematical and computational theories that help to clarify what the notion of optimality might entail in specific classes of networks. The final section deals with optimality in the context of many different high-level issues, including exploring one''s environment, understanding mental illness, linguistic communication, and social organization. The diversity of topics covered in this book is designed to stimulate interdisciplinary thinking and speculation about deep problems in intelligent system organization.

This book is the third in a series based on conferences sponsored by the Metroplex Institute for Neural Dynamics, an interdisciplinary organization of neural network professionals in academia and industry. The topics selected are of broad interest to both those interested in designing machines to perform intelligent functions and those interested in studying how these functions are actually performed by living organisms and generate discussion of basic and controversial issues in the study of mind. The topic of optimality was chosen because it has provoked considerable discussion and controversy in many different academic fields. There are several aspects to the issue of optimality. First, is it true that actual behavior and cognitive functions of living animals, including humans, can be considered as optimal in some sense? Second, what is the utility function for biological organisms, if any, and can it be described mathematically? Rather than organize the chapters on a "biological versus artificial" basis or by what stance they took on optimality, it seemed more natural to organize them either by what level of questions they posed or by what intelligent functions they dealt with. The book begins with some general frameworks for discussing optimality, or the lack of it, in biological or artificial systems. The next set of chapters deals with some general mathematical and computational theories that help to clarify what the notion of optimality might entail in specific classes of networks. The final section deals with optimality in the context of many different high-level issues, including exploring one''s environment, understanding mental illness, linguistic communication, and social organization. The diversity of topics covered in this book is designed to stimulate interdisciplinary thinking and speculation about deep problems in intelligent system organization.