Cognitive Science: Integrative Synchronization Mechanisms in Cognitive Neuroarchitectures of Modern Connectionism

Cover
Title Page
Copyright Page
Dedication
Foreword
Preface
Contents
Introduction: Theory of an Integrative Neurocognition
Abbreviations
Part I: Foundations of Cognitive Science
	1. Cognitive Science: Integrative Theory of Cognition, Cognitivism and Computationalism
		1.1 Basic Concepts, Statements and Subject Area of Cognitive Science
			1.1.1 Neurocognition and Neurocognitive System
			1.1.2 Neurocognitive Structure
			1.1.3 Neurocognitive Process and Neurocognitive Function
			1.1.4 Neurocognitive Synchronization Mechanism
			1.1.5 Computation and Computational (Process-)Algorithm
			1.1.6 Internal Mental Representation
		1.2 Subdisciplines of Cognitive Science
		1.3 Methodology and Methods of Cognitive Science
		1.4 Cognitive Science and (Nonlinear) Dynamical Systems Theory
		1.5 Excursion: Cognitive Turn in Psychology
	2. (General) Theory of (Non-Linear) Dynamical Systems and the Paradigm of Self-Organization
		2.1 (General) System Theory and System Analysis
			2.1.1 (General) System Theory
			2.1.2 Basic Concept of the (Open) System
			2.1.3 System Analysis and System Techniques
		2.2 (Mathematical) Theory of (Nonlinear) Dynamical Systems
			2.2.1 Dynamical Systems Theory
			2.2.2 Minimal Definition of a Dynamical System
			2.2.3 Attractor Theory and Stability Theory in Dynamical Systems
			2.2.4 (Non-)Linearity of a Dynamical System
			2.2.5 Complexity Theory in Dynamical Systems
		2.3 Paradigm of Self-Organization
			2.3.1 Principle of Self-Organization and Emergence
			2.3.2 Theory of Deterministic Chaos
		2.4 Models in the Paradigm of Self-Organization and in the Theory of Dynamical Systems
			2.4.1 Models in Philosophy and in Philosophy of Science
			2.4.2 Models in Natural, Human, Social and Cultural Sciences
	3. Theoretical Paradigms in Cognitive Science and in Theoretical Neurophilosophy
		3.1 Classical Symbol Theory
			3.1.1 Symbolic Method: Symbolic Information Processing on the Basis of Symbols and Symbol Structures
			3.1.2 Local Symbol Representation
			3.1.3 Symbolic Production Systems
			3.1.4 Positive Motivation for Symbolism: Standard Argument by J.A. Fodor and Z.W. Pylyshyn
				3.1.4.1 Productivity of Mental Representations
				3.1.4.2 Systematicity of Mental Representations
				3.1.4.3 Semantic Compositionality of Mental Representations
				3.1.4.4 Systematicity of Inference and Inferential Homogeneity
				3.1.4.5 Representational Theory of Mind and Language of Thought by J.A. Fodor
			3.1.5 Criticism of Symbolism
		3.2 Theory of Connectionism
			3.2.1 Connectionistic Method: Connectionistic Information Processing on the Basis of Vectors and Vector Structures
			3.2.2 Connectionistic Representation and Representational Typology
			3.2.3 Connectionistic Energy Function and Potential Landscape-Metaphor
			3.2.4 Basic Models in Connectionism
				3.2.4.1 Perceptron by F. Rosenblatt
				3.2.4.2 Simple Recurrent Network by J.L. Elman
				3.2.4.3 Hopfield Network by J.J. Hopfield and Linear Associator Model by J.A. Anderson
				3.2.4.4 Self-Organizing (Feature) Map by T. Kohonen
				3.2.4.5 Adaptive Resonance Theory by S. Grossberg and G.A. Carpenter
				3.2.4.6 Classical Architectures in Connectionism
			3.2.5 Methods and Methodical Principles in Connectionism
				3.2.5.1 Multivariate Cluster Analysis
				3.2.5.2 Parallel (Soft) Constraint Satisfaction Modeling
				3.2.5.3 (Stochastic) Gradient Descent Method
				3.2.5.4 Statistical Mechanics Analysis
				3.2.5.5 Attractor Modeling
			3.2.6 Connectionism and Dynamical Systems Theory
			3.2.7 Positive Motivation for Connectionism
			3.2.8 Criticism of Connectionism
	4. Integrative Synchronization Mechanisms and Models in the Cognitive Neurosciences
		4.1 Experimental Methods and Techniques in the Cognitive Neurosciences
		4.2 Methodical Principles and Schemata of the Neuronal Coding in the Cognitive Neurosciences
			4.2.1 (En-)Coding Analysis vs. Decoding Analysis
			4.2.2 Scheme 1: Frequency Coding
			4.2.3 Scheme 2: Temporal Coding
			4.2.4 Scheme 3: Population Coding
			4.2.5 Appendix: Sparse Coding
		4.3 General Binding Problem and Integrative Neuronal Synchronization Mechanisms in the Cognitive Neurosciences
		4.4 Excursion: Integration of Neuronal Information in Visual Perception by Means of the Binding-by-Synchrony Hypothesis by W. Singer et al.
			4.4.1 Neuroanatomical Architecture of the Visual System
			4.4.2 General Binding Problem in the Cognitive Neurosciences
			4.4.3 Sensory (Contour-)Segmentation in the Visual Scene Analysis
			4.4.4 Intramodal Perceptive Integration of other Sensory Systems
			4.4.5 Intermodal Integration of Sensory Information
			4.4.6 Sensomotoric Integration of Neuronal Information
			4.4.7 Neuronal Synchronization and Visual Attention Mechanisms
			4.4.8 Criticism of the Binding-by-Synchrony Hypothesis
			4.4.9 Evaluation
	5. Mathematical, Physical and Neuronal Entropy Based Information Theory
		5.1 Thermodynamical Entropy in Physical Chemistry
		5.2 (Neg-)Entropy Based Information Theory in Mathematics and Physics
		5.3 Structural Analogy to the Harmony Theory by P. Smolensky
		5.4 Information Theoretical Analysis of Neuronal Populations in Cognitive Science
Part II: Cognitive Neuroarchitectures in Neuroinformatics, in Cognitive Science and in Computational Neuroscience
	6. Systematic Class of Classical, Vector-Based Architecture Types
		6.1 Integrated Connectionist/Symbolic Cognitive Architecture by P. Smolensky
			6.1.1 Subsymbolic Paradigm
			6.1.2 Symbolism vs. Connectionism Debate
			6.1.3 Integrated Connectionist/Symbolic (ICS) Cognitive Architecture
			6.1.4 Implications of the ICS Cognitive Architecture
			6.1.5 Criticism and Evaluation of the ICS Cognitive Architecture
		6.2 Holographic Reduced Representations by T.A. Plate
		6.3 Neural Engineering Framework by C. Eliasmith and T.C. Stewart
		6.4 Dynamic Link Architecture by C. von der Malsburg
	7. Systematic Class of Attractor-Based Architecture Types
		7.1 K0-KV Set Attractor Network Models by W.J. Freeman
		7.2 Morphodynamic Attractor Model by J. Petitot
		7.3 Modular Neurodynamical Systems by F. Pasemann
	8. Systematic Class of Oscillator-Based Architecture Types
		8.1 Oscillatory Networks by M. Werning and A. Maye
			8.1.1 Problem of Semantic Compositionality
			8.1.2 Architecture of the Oscillatory Networks
			8.1.3 Algebraic Definition of the Oscillatory Networks
			8.1.4 Emulative (Neuro-)Semantics
			8.1.5 Criticism and Evaluation of the Oscillatory Networks
		8.2 Self-Organizing Neuronal Oscillator Model by P. König and T.B. Schillen
		8.3 Coupled Phase Oscillator Model by H. Finger and P. König
	9. Systematic Class of System Dynamics-Based and Synapse-Based Architecture Types
		9.1 Stochastic Synaptic Model and Liquid Computing by W. Maass and A.M. Zador
		9.2 Modified Stochastic Synaptic Model by K. El-Laithy and M. Bogdan
		9.3 Echo State Network and Reservoir Computing by H. Jaeger
		9.4 (Reward-Modulated) Self-Organizing Recurrent Neural Network by G. Pipa
		9.5 Synfire Chains and Corticonics by M. Abeles
		9.6 Multi-Layered Feed-Forward Networks with Frequency Resonance Mechanism by A. Kumar
	10. Systematic Class of Information Based Architecture Types
		10.1 Functional Clustering Model and Information Integration Theory by G.M. Edelman, G. Tononi and O. Sporns
			10.1.1 Functional Clustering Model by G.M. Edelman and G. Tononi
			10.1.2 Information Integration Theory by G. Tononi and O. Sporns
			10.1.3 Dynamic Core Hypothesis by G.M. Edelman and G. Tononi
		10.2 Harmony Theory by P. Smolensky
		10.3 Free-Energy Principle by K. Friston
	11. Epilogue: Discussion, Evaluation and Future Research
		11.1 Basic Considerations about a Fluent Neurocognition
		11.2 Future Research with Phase Synchronization Mechanisms
			11.2.1 Dynamic Neural Fields (DNF) Architectures
			11.2.2 (Self-Organized) Deep Learning (DL) Architectures
			11.2.3 Neur(-on-)al (Wave) Field Architectures
		11.3 Discussion and Evaluation
		11.4 Outlook on Future Research
		11.5 Implications and Consequences for a Philosophy of Cognitive Science
References
Author Index
Subject Index