Note: Intro -- Preface -- Acknowledgments -- Commentators -- Contents -- Part I Review of Dynamical Brain Theoriesand Experiments -- 1 Introduction---On the Languages of Brains -- 1.1 Brains Are Not Computers -- 1.2 Symbolic Approaches to Brains -- 1.3 Connectionism -- 1.4 Brains as Transient Dynamical Systems -- 1.5 Random Graph Theory (RGT) for Brain Models -- 1.6 Neuropercolation Modeling Paradigm -- References -- 2 Experimental Investigation of High-Resolution Spatio-Temporal Patterns -- 2.1 Method -- 2.1.1 Experiments with Rabbits -- 2.1.2 Human ECoG Experiments -- 2.1.3 Scalp EEG Design Considerations -- 2.2 Temporal Patterns: The Carrier Wave -- 2.3 Spatial Patterns of Amplitude Modulation (AM) and Phase Modulation (PM) -- 2.4 Classification of ECoG and EEG AM Patterns -- 2.5 Characterization of Synchronization-Desynchronization Transitions in the Cortex -- 2.6 Experimental Observation of Singularity -- 2.7 Transmission of Macroscopic Output by Microscopic Pulses -- References -- 3 Interpretation of Experimental Results As Cortical Phase Transitions -- 3.1 Theoretical Approaches to Nonlinear Cortical Dynamics -- 3.2 Scales of Representation: Micro-, Meso-, and Macroscopic Levels -- 3.3 Cinematic Theory of Cortical Phase Transitions -- 3.4 Characterization of Phase Transitions -- 3.4.1 Critical State -- 3.4.2 Singular Dynamics -- 3.4.3 Symmetry Breaking -- 3.4.4 Transition Energy -- 3.4.5 Zero Order Parameter -- 3.4.6 Correlation Length Divergence -- References -- 4 Short and Long Edges in Random Graphs for Neuropil Modeling -- 4.1 Motivation of Using Random Graph Theory for Modeling Cortical Processes -- 4.2 Glossary of Random Graph Terminology -- 4.3 Neuropercolation Basics -- 4.4 Critical Behavior in Neuropercolation with Mean-Field, Local, and Mixed Models -- 4.4.1 Mean-Field Approximation -- 4.4.2 Mixed Short and Long Connections. , 4.5 Finite Size Scaling Theory of Criticality in Brain Models -- References -- 5 Critical Behavior in Hierarchical Neuropercolation Models of Cognition -- 5.1 Basic Principles of Hierarchical Brain Models -- 5.2 Narrow-Band Oscillations in Lattices with Inhibitory Feedback -- 5.3 Broad-Band Oscillations in Coupled Multiple Excitatory-Inhibitory Layers -- 5.4 Exponentially Expanding Graph Model -- References -- 6 Modeling Cortical Phase Transitions Using Random Graph Theory -- 6.1 Describing Brain Networks in Terms of Graph Theory -- 6.1.1 Synchronization and the 'Aha' Moment -- 6.1.2 Practical Considerations on Synchrony -- 6.1.3 Results of Synchronization Measurements -- 6.2 Evolution of Critical Behavior in the Neuropil---a Hypothesis -- 6.3 Singularity and sudden transitions---Interpretation of Experimental Findings -- References -- 7 Summary of Main Arguments -- 7.1 Brain Imaging Combining Structural and Functional MRI, EEG, MEG and Unit Recordings -- 7.2 Significance of RGT for Brain Modeling -- 7.2.1 Relevance to Brain Diseases -- 7.2.2 Neuropercolation as a Novel Mathematical Tool -- 7.3 Neuromorphic Nanoscale Hardware Platforms -- References -- Part II Supplementary Materials on BrainStructure and Dynamics -- 8 Supplement I: Mathematical Framework -- 8.1 ODE Implementation of Freeman K Sets -- 8.1.1 Foundations of Freeman K Sets -- 8.1.2 Hierarchy of Freeman K Sets -- 8.2 Finite-Size Scaling Theory for Random Graphs -- References -- 9 Supplement II: Signal Processing Tools -- 9.1 Description of ECoG and EEG Signals -- 9.2 Hilbert Transform and Analytical Signal Concept for Pattern Analysis -- 9.2.1 Basic Concepts of Analytic Signals -- 9.2.2 Amplitude Modulation (AM) Patterns -- 9.2.3 Frequency Modulation (PM): Temporal Resolution of Frequency -- References -- 10 Supplement III: Neuroanatomy Considerations. , 10.1 Structural Connectivities: Emergence of Neocortex from Allocortex -- 10.2 Constancy of Properties of Neocortex Across Species -- 10.3 Discussion of Scale-Free Structural and Functional Networks -- References -- Part IIICommentaries on NeuroscienceExperiments at Cell and Population Levels -- 11 Commentary by B. Baars -- 11.1 Introduction -- 11.1.1 Does the Cortex ``know'' or ``intend''? -- 11.1.2 Cortical Intention Processing -- 11.1.3 Freeman Neurodynamics -- 11.2 Binocular Rivalry in Primates -- 11.3 Dynamic Global Workspace Theory -- 11.3.1 Direct Evidence for Cortical Binding and Broadcasting -- 11.4 Freeman Neurodynamics -- 11.5 An Integrative Hypothesis -- 11.5.1 Reference Notes -- References -- 12 Commentary by Steven L. Bressler -- 12.1 Introduction -- 12.2 Neuron--Neuron Interactions -- 12.3 Population--Population Interactions -- 12.4 Discussion -- References -- 13 Commentary by Zoltán Somogyvári and Péter Érdi -- 13.1 Modeling Population of Neurons: The Third Option -- 13.2 Mesoscopic Neurodynamics -- 13.2.1 Statistical Neurodynamics: Historical Remarks -- 13.3 Forward and Inverse Modeling of the Neuro-Electric Phenomena -- 13.3.1 Micro-Electric Imaging -- 13.3.2 Source Reconstruction on Single Neurons -- 13.3.3 Anatomical Area and Layer Determination: Micro-Electroanatomy -- 13.4 Conclusions -- References -- 14 Commentary by Frank Ohl -- 14.1 Introduction -- 14.2 Traditional Conceptualizations of Auditory Cortex -- 14.3 Learning-Induced Plasticity in Auditory Cortex and Multisensory Processing -- 14.4 Towards Understanding the Neurodynamics Underlying Perception and Cognition -- 14.5 Exploiting Category Formation to Study the Neurodyamics Underlying the ``Creation of Meaning'' in the Brain -- 14.6 Coexistence of Point-Like Topographic and Field-Like Holographic Representation of Information -- 14.7 Conclusion and Outlook. , References -- Part IV Commentaries on Differential Equationin Cortical Models -- 15 Commentary by James J. Wright -- 15.1 Introduction -- 15.2 Neural Mean-Field Equations -- 15.3 Stochastic Equations in ODE Form -- 15.4 Cortical-Subcortical Interactions -- 15.5 Pulse-Bursting and the Introduction of Stored Information -- 15.6 Synchrony as the Global Attractor -- 15.7 Stimulus-Feature-Linking, Phase Cones, Phase-Transitions, and Null-Spikes -- 15.8 Information Capacity---Synapses and Their Developmental Organization -- 15.9 Cortical Computation and Synchronous Fields -- 15.10 Self-Supervision of Learning -- 15.11 In Conclusion -- References -- 16 Commentary by Hans Liljenström -- 16.1 Introduction -- 16.2 Cortical Network Models -- 16.2.1 Paleocortical Model -- 16.2.2 Neocortical Model -- 16.3 Simulation Results -- 16.3.1 Bottom-Up: Noise-Induced State Transitions -- 16.3.2 Top-Down: Network Modulation of Neural Activity -- 16.4 Discussion -- References -- 17 Commentary by Ray Brown and Morris Hirsch -- 17.1 Introduction -- 17.2 Stretching and Folding Provide an Alternative Approach to the Laws of Physics for Modeling Dynamics -- 17.3 Infinitesimal Diffeomorphisms First Originated from Integral Equations -- 17.4 Deriving IDEs for the KIII Model -- 17.4.1 The Linear ID Provides Fundamental Insights into the Dynamics of Stretching and Folding Systems -- 17.4.2 The Standard KIII Model Can Be Reformulated as a Set of Infinitesimal Diffeomorphisms (ID) -- 17.5 The Application of IDs to K-Neurodynamics May Result in Useful Simplifications of the ODEs Use to Describe the KIII System -- 17.6 The KIII-ID Model Can Provide a Reduction in Computation as Well as Insights into the Neurodynamics -- 17.7 The Wave Ψ(X) for Any K Model May Arise from Partial Differential Equations that Must Be Derived from Experiment -- 17.8 Summary -- References. , 18 Commentary by Ray Brown on Real World Applications -- 18.1 Introduction -- 18.2 Implementation of the KIII Model -- 18.3 Selection of Mesoscopic Components -- 18.4 Example Results -- 18.5 Summary -- References -- Part VCommentaries on New Theoriesof Cortical Dynamics and Cognition -- 19 Commentary by Paul J. Werbos -- 19.1 Introduction -- 19.2 Top Down Versus Bottom up and the Neuron Dogma -- 19.3 An Approach to Explaining the 4--8 Hertz Abrupt Shifts in Cortex -- 19.4 Could Field Effects Be Important to Brain and Mind? -- 19.4.1 Associate Memory or Quantum Effects Inside the Neuron -- 19.4.2 Dendritic Field Processing -- 19.4.3 Quantum Fields and Quantum Mind -- 19.5 Summary and Conclusions -- References -- 20 Commentary by Ichiro Tsuda -- 20.1 Self-organization and Field Theory -- 20.2 Differentiation by Variational Principle -- References -- 21 Commentary by Kazuyuki Aihara and Timothée Leleu -- 21.1 Introduction -- 21.2 Propagation of Patterns in Modular Networks -- References -- 22 Commentary by Giuseppe Vitiello -- 22.1 The Brain Is Not a Stupid Star -- 22.2 Far from the Equilibrium Systems -- 22.3 Conclusion -- References -- Epilogue -- Index.

Note: Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Acknowledgements -- Prologue -- Chapter 1 Brains and Minds -- 1.1 The origin and growth of introspection -- 1.2 Artificial intelligence (AI) and cognition -- 1.3 Brain studies in the material world -- 1.4 Medicine, philosophy, and intentionality -- 1.5 The growth of modern neuroscience -- 1.6 Too many brain data with inadequate metaphysics -- 1.7 A fresh start based in nonlinear dynamics -- 1.8 Summary -- Chapter 2 Nerve Energy and Neuroactivity -- 2.1 Dynamics and Descartes -- 2.2 The reflex as an input-output relation -- 2.3 Nerve energy replaces vis nervorum -- 2.4 Fields of nerve energy -- 2.5 Information replaces nerve energy -- 2.6 Extension of brain theory to the quantum level -- 2.7 Neuropil, Neuroactivity, and the "new laws -- 2.8 Neuroactivity and neurodynamics -- 2.9 Summary -- Chapter 3 Sensation and Perception -- 3.1 Remarks on the use of dynamics -- 3.2 Input-output of neurons: Forward transmission -- 3.3 Neuron populations: Feedback transmission -- 3.4 Transfers of activity across hierarchical levels -- 3.5 Observation, analysis at the microscopic level -- 3.6 Observation, analysis at the macroscopic level -- 3.7 Distinguishing reception and perception -- 3.8 Neural mechanism of read-out of bulbar patterns -- 3.9 Modeling neural dynamics from pulses and EEGs -- 3.10 Basin-attractor theory -- 3.11 Implications of neurodynamics for solipsism -- 3.12 Summary -- Chapter 4 Intention and Movement -- 4.1 Brain architectures -- 4.2 The architecture of intention -- 4.3 Objective orientation in space and time -- 4.4 The limbic system -- 4.5 Subjective orientation in space and time -- 4.6 Reafference and motor control -- 4.7 The reticular formation and arousal -- 4.8 Communication between brains -- 4.9 Summary -- Chapter 5 Intentional Structure and Thought. , 5.1 Classification by multivariate statistics -- 5.2 Context dependence and pattern variability -- 5.3 Other aspects of Neuroactivity and behavior -- 5.4 Forced state transitions and cortical itinerancy -- 5.5 Neural activity patterns and representations -- 5.6 Continuous versus discrete operations: Graining -- 5.7 Thoughts and idols -- 5.8 Age, rigidity, and wisdom in brain function -- 5.9 Summary -- Chapter 6 Learning and Unlearning -- 6.1 Learning and self-organization -- 6.2 The neural basis for learning -- 6.3 Artificial neural networks and digital computers -- 6.4 Arousal, motivation, and reinforcement -- 6.5 Isolation, unlearning, and pair bonding -- 6.6 Conversion and social bonding -- 6.7 Function and malfunction in unlearning -- 6.8 Dance as the biotechnology of group formation -- 6.9 Summary -- Chapter 7 Self and Society -- 7.1 Consciousness -- 7.2 The self-organizing self -- 7.3 Self, reafference, and causality -- 7.4 The social utility of self and cause -- 7.5 The qualia of learning and unlearning -- 7.6 Spiritual leaders and psychedelic tourists -- 7.7 Summary -- Epilogue -- Notes -- 2.1 Information theory applied to arrays of neurons -- 2.2 Ecological maps of affordance and effectivity -- 2.3 The "quality of understanding" in Roger Penrose -- 2.4 Neural activity versus Neuroactivity -- 3.1 The theory of measurement in neurobiology -- 3.2 Peripheral versus central "codes -- 3.3 Quantitative relations between pulses and waves -- 3.4 Population pulse-wave relations: Sigmoid curve -- 3.5 Further comments on the binding hypothesis -- 3.6 Surgical, pharmacological analysis of olfaction -- 3.7 Macroscopic activity in multi-pulse recordings -- 3.8 Theories of olfaction: The utility of oscillation -- 3.9 Chaos, randomness, noise: Digital determinism -- 3.10 Anomalous dispersion : A means of phase locking. , 4.1 Neuropil in the forebrain of salamanders -- 4.2 Maclean's Triune Brain: Reason versus emotion -- 4.3 Engrams, local storage, and Wilder Penfield -- 4.4 Range contral in multisensory convergence -- 4.5 Sensory neglect, body image, phantom limb -- 4.6 Synesthesia: the complexity of cerebral cortex -- 4.7 "Deep structure": Chomsky versus Piaget -- 5.1 Three failed approaches to building brains -- 5.2 Stabilizing aperiodic cortical neural activity -- 5.3 Definitions of thought: Heidegger versus Piaget -- 6.1 An example of the value of music in society -- 7.1 A complement to the Turing test: Play games -- 7.2 Kinesthetics, 4-D geometry, and virtual reality -- 7.3 The madness of love: Shakespeare versus Plato -- References and Author Index -- Subject Index and Glossary.