Neural Assemblies: An Alternative Approach to Classical Artificial Intelligence

Description

In the new edition of Neural Assemblies, the author places his original ideas and motivations within the framework of modern and cognitive neuroscience and gives a short and focused overview of the development of computational neuroscience and artificial neural networks over the last 40 years.  In this book the author develops a theory of how the human brain might function. Starting with a motivational introduction to the brain as an organ of information processing, he presents a computational perspective on the basic concepts and ideas of neuroscience research on the underlying principles of brain function. In addition, the reader is introduced to the most important methods from computer science and mathematical modeling that are required for a computational understanding of information processing in the brain. Written by an expert in the field of neural information processing, this book offers a personal historical view of the development of artificial intelligence, artificial neural networks, and computational cognitive neuroscience over the last 40 years, with a focus on the realization of higher cognitive functions rather than more peripheral sensory or motor organization. The book is therefore aimed at students and researchers who want to understand how the basic neuroscientific and computational concepts in the study of brain function have changed over the last decades. Preface to the First Edition Preface to the Second Edition About This Book Contents About the Author Part I: Basic Facts and Ideas for a Brain 1: The Brain Is an Organ for Information Processing 1.1 Introduction 1.2 The Flow of Information 1.3 Thinking Seen from Within and from Without 1.4 Further Developments References 2: The Organization and Improvement of Behavior 2.1 Introduction 2.2 How to Build Well-Behaving Machines 2.3 Organizations, Algorithms, and Flow Diagrams 2.4 The Improved Matchbox Algorithm 2.5 Further Comments References 3: A Neuronal Realization of the Survival Algorithm 3.1 Introduction 3.2 The Survival Algorithm as a Model of an Animal 3.3 Specifying the Survival Algorithm 3.4 Further Developments Appendix: Local Synaptic Rules References 4: On the Structure and Function of Cortical Areas 4.1 Introduction 4.2 The Anatomy of the Cortical Connectivity 4.3 The Visual Input to the Cortex 4.4 Changes in the Cortex with Learning 4.5 Further Developments References Part II: Thinking in Cell Assemblies 5: Cognitive Mental Processes Realized by Cell Assemblies in the Cortex 5.1 Introduction 5.2 From Neural Dynamics to Cell Assemblies 5.3 Introspection and the Rules of Threshold Control 5.3.1 Thinking In Terms Of Cell Assemblies (Hebb 1949, 1958) and Threshold Control (Braitenberg 1978) 5.4 Further Speculations 5.5 Further Developments Appendix: Cell Assemblies: The Basic Ideas References 6: A Model of Language Understanding by Interacting Cortical Areas 6.1 Introduction 6.2 The Cortical Machinery for Language Understanding Modeling Principles 6.3 Sentence Understanding by the ``Language Modules´´ 6.4 Disambiguation 6.5 Discussion and Perspectives Appendix: Global Computation in an Associative Network of Cortical Areas References 7: Can We Accept a Mechanistic Description of Our Cognitive Mental Processes? 7.1 Introduction 7.2 Men, Monkeys, and Machines 7.3 Why All These Speculations? References Part III: Further Developments Until Today 8: Developments in Computer Science and Technical Applications 8.1 Learning in Artificial Neural Networks 8.2 Applications and Architectures 8.3 How Is All This Related to This Book? References 9: New Results from Brain Research and Neuroscience 9.1 Experimental Advances 9.2 How Has All This Affected the Picture I Developed in 1982? 9.3 Towards a Bigger Picture Appendix: Computational Cognitive Neuroscience References 10: The Development of Brain Theory 10.1 Introduction 10.2 Theory and Observation 10.3 Particular Issues with Modeling in Biology and Neuroscience 10.4 Large-Scale Brain Modeling and Simulation 10.4.1 Statistical Approach 10.4.2 Technological Approach 10.4.3 Computational Approach 10.5 Large Computational Models in Cognitive Neuroscience References 11: Do We Need Cognitive Neuroscience? 11.1 Cognitive Neuroscience and Humanity 11.2 Human-Computer Interaction and Artificial Companions 11.3 Artificial Autonomous Agents References