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Cognitive Phase Transitions in the Cerebral Cortex - Enhancing the Neuron Doctrine by Modeling Neural Fields 1st ed. 2016 [Hardback]

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  • Formāts: Hardback, 262 pages, height x width: 235x155 mm, weight: 5561 g, XXV, 262 p., 1 Hardback
  • Sērija : Studies in Systems, Decision and Control 39
  • Izdošanas datums: 06-Nov-2015
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3319244043
  • ISBN-13: 9783319244044
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Cognitive Phase Transitions in the Cerebral Cortex - Enhancing the Neuron Doctrine by Modeling Neural Fields 1st ed. 2016Palielināt
  • Formāts: Hardback, 262 pages, height x width: 235x155 mm, weight: 5561 g, XXV, 262 p., 1 Hardback
  • Sērija : Studies in Systems, Decision and Control 39
  • Izdošanas datums: 06-Nov-2015
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3319244043
  • ISBN-13: 9783319244044
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783319244044.html
Keywords:
This intriguing book was born out of the many discussions the authors had in the past 10 years about the role of scale-free structure and dynamics in producing intelligent behavior in brains.The microscopic dynamics of neural networks is well described by the prevailing paradigm based in a narrow interpretation of the neuron doctrine. This book broadens the doctrine by incorporating the dynamics of neural fields, as first revealed by modeling with differential equations (K-sets). The book broadens that approach by application of random graph theory (neuropercolation). The book concludes with diverse commentaries that exemplify the wide range of mathematical/conceptual approaches to neural fields.This book is intended for researchers, postdocs, and graduate students, who see the limitations of network theory and seek a beachhead from which to embark on mesoscopic and macroscopic neurodynamics.

Part I Review of Dynamical Brain Theories and Experiments.- Part II Supplementary Materials on Brain Structure and Dynamics.-Part III Commentaries.
Part I Review of Dynamical Brain Theories and Experiments
1 Introduction---On the Languages of Brains
3(12)
1.1 Brains Are Not Computers
3(1)
1.2 Symbolic Approaches to Brains
4(1)
1.3 Connectionism
5(2)
1.4 Brains as Transient Dynamical Systems
7(1)
1.5 Random Graph Theory (RGT) for Brain Models
8(1)
1.6 Neuropercolation Modeling Paradigm
9(6)
References
10(5)
2 Experimental Investigation of High-Resolution Spatio-Temporal Patterns
15(20)
2.1 Method
15(3)
2.1.1 Experiments with Rabbits
15(1)
2.1.2 Human ECoG Experiments
16(1)
2.1.3 Scalp EEG Design Considerations
17(1)
2.2 Temporal Patterns: The Carrier Wave
18(2)
2.3 Spatial Patterns of Amplitude Modulation (AM) and Phase Modulation (PM)
20(3)
2.4 Classification of ECoG and EEG AM Patterns
23(2)
2.5 Characterization of Synchronization-Desynchronization Transitions in the Cortex
25(1)
2.6 Experimental Observation of Singularity
26(2)
2.7 Transmission of Macroscopic Output by Microscopic Pulses
28(7)
References
30(5)
3 Interpretation of Experimental Results As Cortical Phase Transitions
35(12)
3.1 Theoretical Approaches to Nonlinear Cortical Dynamics
35(2)
3.2 Scales of Representation: Micro-, Meso-, and Macroscopic Levels
37(1)
3.3 Cinematic Theory of Cortical Phase Transitions
38(3)
3.4 Characterization of Phase Transitions
41(6)
3.4.1 Critical State
41(1)
3.4.2 Singular Dynamics
41(1)
3.4.3 Symmetry Breaking
42(1)
3.4.4 Transition Energy
42(1)
3.4.5 Zero Order Parameter
42(1)
3.4.6 Correlation Length Divergence
43(1)
References
43(4)
4 Short and Long Edges in Random Graphs for Neuropil Modeling
47(16)
4.1 Motivation of Using Random Graph Theory for Modeling Cortical Processes
47(1)
4.2 Glossary of Random Graph Terminology
48(3)
4.3 Neuropercolation Basics
51(3)
4.4 Critical Behavior in Neuropercolation with Mean-Field, Local, and Mixed Models
54(4)
4.4.1 Mean-Field Approximation
54(2)
4.4.2 Mixed Short and Long Connections
56(2)
4.5 Finite Size Scaling Theory of Criticality in Brain Models
58(5)
References
58(5)
5 Critical Behavior in Hierarchical Neuropercolation Models of Cognition
63(8)
5.1 Basic Principles of Hierarchical Brain Models
63(1)
5.2 Narrow-Band Oscillations in Lattices with Inhibitory Feedback
64(1)
5.3 Broad-Band Oscillations in Coupled Multiple Excitatory-Inhibitory Layers
65(1)
5.4 Exponentially Expanding Graph Model
66(5)
References
68(3)
6 Modeling Cortical Phase Transitions Using Random Graph Theory
71(8)
6.1 Describing Brain Networks in Terms of Graph Theory
71(3)
6.1.1 Synchronization and the `Aha' Moment
71(1)
6.1.2 Practical Considerations on Synchrony
72(2)
6.1.3 Results of Synchronization Measurements
74(1)
6.2 Evolution of Critical Behavior in the Neuropil---a Hypothesis
74(3)
6.3 Singularity and Sudden Transitions---Interpretation of Experimental Findings
77(2)
References
77(2)
7 Summary of Main Arguments
79(10)
7.1 Brain Imaging Combining Structural and Functional MRI, EEG, MEG and Unit Recordings
79(1)
7.2 Significance of RGT for Brain Modeling
79(4)
7.2.1 Relevance to Brain Diseases
80(1)
7.2.2 Neuropercolation as a Novel Mathematical Tool
81(2)
7.3 Neuromorphic Nanoscale Hardware Platforms
83(6)
References
84(5)
Part II Supplementary Materials on Brain Structure and Dynamics
8 Supplement I: Mathematical Framework
89(12)
8.1 ODE Implementation of Freeman K Sets
89(7)
8.1.1 Foundations of Freeman K Sets
89(2)
8.1.2 Hierarchy of Freeman K Sets
91(5)
8.2 Finite-Size Scaling Theory for Random Graphs
96(5)
References
98(3)
9 Supplement II: Signal Processing Tools
101(6)
9.1 Description of ECoG and EEG Signals
101(1)
9.2 Hilbert Transform and Analytical Signal Concept for Pattern Analysis
102(5)
9.2.1 Basic Concepts of Analytic Signals
102(1)
9.2.2 Amplitude Modulation (AM) Patterns
103(1)
9.2.3 Frequency Modulation (PM): Temporal Resolution of Frequency
104(1)
References
105(2)
10 Supplement III: Neuroanatomy Considerations
107
10.1 Structural Connectivities: Emergence of Neocortex from Allocortex
107(2)
10.2 Constancy of Properties of Neocortex Across Species
109(2)
10.3 Discussion of Scale-Free Structural and Functional Networks
111
References
112
6.2 Evolution of Critical Behavior in the Neuropil---a Hypothesis
74(3)
6.3 Singularity and Sudden Transitions---Interpretation of Experimental Findings
77(2)
References
77(2)
7 Summary of Main Arguments
79(10)
7.1 Brain Imaging Combining Structural and Functional MRI, EEG, MEG and Unit Recordings
79(1)
7.2 Significance of RGT for Brain Modeling
79(4)
7.2.1 Relevance to Brain Diseases
80(1)
7.2.2 Neuropercolation as a Novel Mathematical Tool
81(2)
7.3 Neuromorphic Nanoscale Hardware Platforms
83(6)
References
84(5)
Part II Supplementary Materials on Brain Structure and Dynamics
8 Supplement I: Mathematical Framework
89(12)
8.1 ODE Implementation of Freeman K Sets
89(7)
8.1.1 Foundations of Freeman K Sets
89(2)
8.1.2 Hierarchy of Freeman K Sets
91(5)
8.2 Finite-Size Scaling Theory for Random Graphs
96(5)
References
98(3)
9 Supplement II: Signal Processing Tools
101(6)
9.1 Description of ECoG and EEG Signals
101(1)
9.2 Hilbert Transform and Analytical Signal Concept for Pattern Analysis
102(5)
9.2.1 Basic Concepts of Analytic Signals
102(1)
9.2.2 Amplitude Modulation (AM) Patterns
103(1)
9.2.3 Frequency Modulation (PM): Temporal Resolution of Frequency
104(1)
References
105(2)
10 Supplement III: Neuroanatomy Considerations
107(10)
10.1 Structural Connectivities: Emergence of Neocortex from Allocortex
107(2)
10.2 Constancy of Properties of Neocortex Across Species
109(2)
10.3 Discussion of Scale-Free Structural and Functional Networks
111(6)
References
112(5)
Part III Commentaries on Neuroscience Experiments at Cell and Population Levels
11 Commentary by B. Baars
117(10)
11.1 Introduction
118(2)
11.1.1 Does the Cortex "know" or "intend"?
118(1)
11.1.2 Cortical Intention Processing
119(1)
11.1.3 Freeman Neurodynamics
119(1)
11.2 Binocular Rivalry in Primates
120(1)
11.3 Dynamic Global Workspace Theory
121(2)
11.3.1 Direct Evidence for Cortical Binding and Broadcasting
122(1)
11.4 Freeman Neurodynamics
123(1)
11.5 An Integrative Hypothesis
124(3)
11.5.1 Reference Notes
124(1)
References
124(3)
12 Commentary by Steven L. Bressler
127(8)
12.1 Introduction
127(2)
12.2 Neuron-Neuron Interactions
129(1)
12.3 Population-Population Interactions
130(1)
12.4 Discussion
131(4)
References
132(3)
13 Commentary by Zoltan Somogyvari and Peter Erdi
135(12)
13.1 Modeling Population of Neurons: The Third Option
135(1)
13.2 Mesoscopic Neurodynamics
136(2)
13.2.1 Statistical Neurodynamics: Historical Remarks
136(2)
13.3 Forward and Inverse Modeling of the Neuro-Electric Phenomena
138(5)
13.3.1 Micro-Electric Imaging
139(1)
13.3.2 Source Reconstruction on Single Neurons
140(1)
13.3.3 Anatomical Area and Layer Determination: Micro-Electroanatomy
140(3)
13.4 Conclusions
143(4)
References
144(3)
14 Commentary by Frank Ohl
147(16)
14.1 Introduction
147(1)
14.2 Traditional Conceptualizations of Auditory Cortex
148(1)
14.3 Learning-Induced Plasticity in Auditory Cortex and Multisensory Processing
149(1)
14.4 Towards Understanding the Neurodynamics Underlying Perception and Cognition
150(1)
14.5 Exploiting Category Formation to Study the Neurodyamics Underlying the "Creation of Meaning" in the Brain
150(4)
14.6 Coexistence of Point-Like Topographic and Field-Like Holographic Representation of Information
154(3)
14.7 Conclusion and Outlook
157(6)
References
157(6)
Part IV Commentaries on Differential Equation in Cortical Models
15 Commentary by James J. Wright
163(14)
15.1 Introduction
163(1)
15.2 Neural Mean-Field Equations
164(2)
15.3 Stochastic Equations in ODE Form
166(1)
15.4 Cortical-Subcortical Interactions
167(1)
15.5 Pulse-Bursting and the Introduction of Stored Information
168(1)
15.6 Synchrony as the Global Attractor
169(1)
15.7 Stimulus-Feature-Linking, Phase Cones, Phase-Transitions, and Null-Spikes
169(1)
15.8 Information Capacity---Synapses and Their Developmental Organization
170(1)
15.9 Cortical Computation and Synchronous Fields
171(1)
15.10 Self-Supervision of Learning
172(1)
15.11 In Conclusion
173(4)
References
173(4)
16 Commentary by Hans Liljenstrom
177(10)
16.1 Introduction
177(1)
16.2 Cortical Network Models
178(2)
16.2.1 Paleocortical Model
179(1)
16.2.2 Neocortical Model
179(1)
16.3 Simulation Results
180(3)
16.3.1 Bottom-Up: Noise-Induced State Transitions
180(1)
16.3.2 Top-Down: Network Modulation of Neural Activity
181(2)
16.4 Discussion
183(4)
References
185(2)
17 Commentary by Ray Brown and Morris Hirsch
187(18)
17.1 Introduction
187(2)
17.2 Stretching and Folding Provide an Alternative Approach to the Laws of Physics for Modeling Dynamics
189(2)
17.3 Infinitesimal Diffeomorphisms First Originated from Integral Equations
191(3)
17.4 Deriving IDEs for the KIII Model
194(3)
17.4.1 The Linear ID Provides Fundamental Insights into the Dynamics of Stretching and Folding Systems
195(1)
17.4.2 The Standard KIII Model Can Be Reformulated as a Set of Infinitesimal Diffeomorphisms (ID)
196(1)
17.5 The Application of IDs to K-Neurodynamics May Result in Useful Simplifications of the ODEs Use to Describe the KIII System
197(2)
17.6 The KIII-ID Model Can Provide a Reduction in Computation as Well as Insights into the Neurodynamics
199(3)
17.7 The Wave Ψ(X) for Any K Model May Arise from Partial Differential Equations that Must Be Derived from Experiment
202(2)
17.8 Summary
204(1)
References
204(1)
18 Commentary by Ray Brown on Real World Applications
205(12)
18.1 Introduction
205(1)
18.2 Implementation of the KIII Model
206(1)
18.3 Selection of Mesoscopic Components
206(3)
18.4 Example Results
209(3)
18.5 Summary
212(5)
References
213(4)
Part V Commentaries on New Theories of Cortical Dynamics and Cognition
19 Commentary by Paul J. Werbos
217(12)
19.1 Introduction
217(1)
19.2 Top Down Versus Bottom up and the Neuron Dogma
218(3)
19.3 An Approach to Explaining the 4--8 Hertz Abrupt Shifts in Cortex
221(2)
19.4 Could Field Effects Be Important to Brain and Mind?
223(4)
19.4.1 Associate Memory or Quantum Effects Inside the Neuron
223(1)
19.4.2 Dendritic Field Processing
224(1)
19.4.3 Quantum Fields and Quantum Mind
225(2)
19.5 Summary and Conclusions
227(2)
References
228(1)
20 Commentary by Ichiro Tsuda
229(4)
20.1 Self-organization and Field Theory
229(1)
20.2 Differentiation by Variational Principle
230(3)
References
232(1)
21 Commentary by Kazuyuki Aihara and Timothee Leleu
233(6)
21.1 Introduction
233(1)
21.2 Propagation of Patterns in Modular Networks
234(5)
References
237(2)
22 Commentary by Giuseppe Vitiello
239(12)
22.1 The Brain Is Not a Stupid Star
240(3)
22.2 Far from the Equilibrium Systems
243(2)
22.3 Conclusion
245(6)
References
248(3)
Epilogue 251(4)
Index 255