Criticality in Neural Systems by Schuster, Heinz Georg -- Read -- Imperial Library of Trantor

Index

Cover Reviews of Nonlinear Dynamics and Complexity Title Page Copyright List of Contributors Chapter 1: Introduction

1.1 Criticality in Neural Systems

Chapter 2: Criticality in Cortex: Neuronal Avalanches and Coherence Potentials

2.1 The Late Arrival of Critical Dynamics to the Study of Cortex Function 2.2 Cortical Resting Activity Organizes as Neuronal Avalanches 2.3 Neuronal Avalanches: Cascades of Cascades 2.4 The Statistics of Neuronal Avalanches and Earthquakes 2.5 Neuronal Avalanches and Cortical Oscillations 2.6 Neuronal Avalanches Optimize Numerous Network Functions 2.7 The Coherence Potential: Threshold-Dependent Spread of Synchrony with High Fidelity 2.8 The Functional Architecture of Neuronal Avalanches and Coherence Potentials Acknowledgement References

Chapter 3: Critical Brain Dynamics at Large Scale

3.1 Introduction 3.2 What is Criticality Good for? 3.3 Statistical Signatures of Critical Dynamics 3.4 Beyond Averages: Spatiotemporal Brain Dynamics at Criticality 3.5 Consequences 3.6 Summary and Outlook References

Chapter 4: The Dynamic Brain in Action: Coordinative Structures, Criticality, and Coordination Dynamics

4.1 Introduction 4.2 The Organization of Matter 4.3 Setting the Context: A Window into Biological Coordination 4.4 Beyond Analogy 4.5 An Elementary Coordinative Structure: Bimanual Coordination 4.6 Theoretical Modeling: Symmetry and Phase Transitions 4.7 Predicted Signatures of Critical Phenomena in Biological Coordination 4.8 Some Comments on Criticality, Timescales, and Related Aspects 4.9 Symmetry Breaking and Metastability 4.10 Nonequilibrium Phase Transitions in the Human Brain: MEG, EEG, and fMRI 4.11 Neural Field Modeling of Multiple States and Phase Transitions in the Brain 4.12 Transitions, Transients, Chimera, and Spatiotemporal Metastability 4.13 The Middle Way: Mesoscopic Protectorates 4.14 Concluding Remarks Acknowledgments References

Chapter 5: The Correlation of the Neuronal Long-Range Temporal Correlations, Avalanche Dynamics with the Behavioral Scaling Laws and Interindividual Variability

5.1 Introduction 5.2 Criticality in the Nervous System: Behavioral and Physiological Evidence 5.3 Magneto- and Electroencephalography (M/EEG) as a Tool for Noninvasive Reconstruction of Human Cortical Dynamics 5.4 Slow Neuronal Fluctuations: The Physiological Substrates of LRTC 5.6 Neuronal Avalanches, LRTC, and Oscillations: Enigmatic Coexistence? 5.7 Conclusions Acknowledgments References

Chapter 6: The Turbulent Human Brain: An MHD Approach to the MEG

6.1 Introduction 6.2 Autonomous, Intermittent, Hierarchical Motions, from Brain Proteins Fluctuations to Emergent Magnetic Fields 6.3 Magnetic Field Induction and Turbulence; Its Maintenance, Decay, and Modulation 6.4 Localizing a Time-Varying Entropy Measure of Turbulence, Rank Vector Entropy (RVE) [35] [107], Using a Linearly Constrained Minimum Variance (LCMV) Beamformer Such as Synthetic Aperture Magnetometry (SAM) [25] [34], Yields State and Function-Related Localized Increases and Decreases in the RVE Estimate 6.5 Potential Implications of the MHD Approach to MEG Magnetic Fields for Understanding the Mechanisms of Action and Clinical Applications of the Family of TMS (Transcranial Magnetic Stimulation) Human Brain Therapies References

Chapter 7: Thermodynamic Model of Criticality in the Cortex Based on EEG/ECoG Data

7.1 Introduction 7.2 Principles of Hierarchical Brain Models 7.3 Mathematical Formulation of Neuropercolation 7.4 Critical Regimes of Coupled Hierarchical Lattices 7.5 BroadBand Chaotic Oscillations 7.6 Conclusions References

Chapter 8: Neuronal Avalanches in the Human Brain

8.1 Introduction 8.2 Data and Cascade-Size Analysis 8.3 Cascade-Size Distributions are Power Laws 8.4 The Data are Captured by a Critical Branching Process 8.5 Discussion 8.6 Summary Acknowledgements References

Chapter 9: Critical Slowing and Perception

9.1 Introduction 9.2 Itinerant Dynamics 9.3 The Free Energy Principle 9.4 Neurobiological Implementation of Active Inference 9.5 Self-Organized Instability 9.6 Birdsong, Attractors, and Critical Slowing 9.7 Conclusion References

Chapter 10: Self-Organized Criticality in Neural Network Models

10.1 Introduction 10.2 Avalanche Dynamics in Neuronal Systems 10.3 Simple Models for Self-Organized Critical Adaptive Neural Networks 10.4 Conclusion Acknowledgments References

Chapter 11: Single Neuron Response Fluctuations: A Self-Organized Criticality Point of View

11.1 Neuronal Excitability 11.2 Experimental Observations on Excitability Dynamics 11.3 Self-Organized Criticality Interpretation 11.4 Adaptive Rates and Contact Processes 11.5 Concluding Remarks References

Chapter 12: Activity Dependent Model for Neuronal Avalanches

12.1 The Model 12.2 Neuronal Avalanches in Spontaneous Activity 12.3 Learning 12.4 Temporal Organization of Neuronal Avalanches 12.5 Conclusions References

Chapter 13: The Neuronal Network Oscillation as a Critical Phenomenon

13.1 Introduction 13.2 Properties of Scale-Free Time Series 13.3 The Detrended Fluctuation Analysis (DFA) 13.4 DFA Applied to Neuronal Oscillations 13.5 Insights from the Application of DFA to Neuronal Oscillations 13.6 Scaling Behavior of Oscillations: a Sign of Criticality? Acknowledgment References

Chapter 14: Critical Exponents, Universality Class, and Thermodynamic “Temperature” of the Brain

14.1 Introduction 14.2 Thermodynamic Quantities at the Critical Point and Their Neuronal Interpretations 14.3 Finite-Size Scaling 14.4 Studying the Thermodynamics Properties of Neuronal Avalanches at Different Scales 14.5 What Could be the “Temperature” for the Brain? Acknowledgment References

Chapter 15: Peak Variability and Optimal Performance in Cortical Networks at Criticality

15.1 Introduction 15.2 Fluctuations are Highest Near Criticality 15.3 Variability of Spatial Activity Patterns 15.4 Variability of Phase Synchrony 15.5 High Variability, but Not Random 15.6 Functional Implications of High Entropy of Ongoing Cortex Dynamics

Chapter 16: Criticality at Work: How Do Critical Networks Respond to Stimuli?

16.1 Introduction 16.2 Responding to Stimuli 16.3 Concluding Remarks Acknowledgements

Chapter 17: Critical Dynamics in Complex Networks

17.1 Introduction: Critical Branching Processes 17.2 Description and Properties of Networks 17.3 Branching Processes in Complex Networks 17.4 Discussion References

Chapter 18: Mechanisms of Self-Organized Criticality in Adaptive Networks

18.1 Introduction 18.2 Basic Considerations 18.3 A Toy Model 18.4 Mechanisms of Self-Organization 18.5 Implications for Information Processing 18.6 Discussion References

Chapter 19: Cortical Networks with Lognormal Synaptic Connectivity and Their Implications in Neuronal Avalanches

19.1 Introduction 19.2 Critical Dynamics in Neuronal Wiring Development 19.3 Stochastic Resonance by Highly Inhomogeneous Synaptic Weights on Spike Neurons 19.4 SSWD Recurrent Networks Generate Optimal Intrinsic Noise 19.5 Incorporation of Local Clustering Structure 19.6 Emergence of Bistable States in the Clustered Network 19.7 Possible Implications of SSWD Networks for Neuronal Avalanches 19.8 Summary Acknowledgment References

Chapter 20: Theoretical Neuroscience of Self-Organized Criticality: From Formal Approaches to Realistic Models

20.1 Introduction 20.2 The Eurich Model of Criticality in Neural Networks 20.3 LHG Model: Dynamic Synapses Control Criticality 20.4 Criticality by Homeostatic Plasticity 20.5 Conclusion Acknowledgment References

Chapter 21: Nonconservative Neuronal Networks During Up-States Self-Organize Near Critical Points

21.1 Introduction 21.2 Model 21.3 Simulations 21.4 Heterogeneous Synapses 21.5 Conclusion Acknowledgment References

Chapter 22: Self-Organized Criticality and Near-Criticality in Neural Networks

22.1 Introduction 22.2 A Neural Network Exhibiting Self-Organized Criticality 22.3 Excitatory and Inhibitory Neural Network Dynamics 22.4 An E–I Neural Network Exhibiting Self-Organized Near-Criticality 22.5 Discussion Acknowledgements References

Chapter 23: Neural Dynamics: Criticality, Cooperation, Avalanches, and Entrainment between Complex Networks

23.1 Introduction 23.2 Decision-Making Model (DMM) at Criticality 23.3 Neural Dynamics 23.4 Avalanches and Entrainment 23.5 Concluding Remarks References

Chapter 24: Complex Networks: From Social Crises to Neuronal Avalanches

24.1 Introduction 24.2 The Decision-Making Model (DMM) 24.3 Topological Complexity 24.4 Temporal Complexity 24.5 Inflexible Minorities 24.6 Conclusions References

Chapter 25: The Dynamics of Neuromodulation

25.1 Introduction 25.2 Background 25.3 Discussion and Conclusions 25.4 A Final Thought 25.5 Summary References

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