Criticality in Neural Systems E-Book

• Herausgeber: Wiley-VCH
• Kategorie: Wissenschaft und neue Technologien
• Serie: Reviews of Nonlinear Dynamics and Complexity
• Sprache: Englisch

Seitenzahl: 1102

Veröffentlichungsjahr: 2014

Beschreibung

Neurowissenschaftler suchen nach Antworten auf die Fragen, wie wir lernen und Information speichern, welche Prozesse im Gehirn verantwortlich sind und in welchem Zeitrahmen diese ablaufen. Die Konzepte, die aus der Physik kommen und weiterentwickelt werden, können in Medizin und Soziologie, aber auch in Robotik und Bildanalyse Anwendung finden. Zentrales Thema dieses Buches sind die sogenannten kritischen Phänomene im Gehirn. Diese werden mithilfe mathematischer und physikalischer Modelle beschrieben, mit denen man auch Erdbeben, Waldbrände oder die Ausbreitung von Epidemien modellieren kann. Neuere Erkenntnisse haben ergeben, dass diese selbstgeordneten Instabilitäten auch im Nervensystem auftreten. Dieses Referenzwerk stellt theoretische und experimentelle Befunde internationaler Gehirnforschung vor zeichnet die Perspektiven dieses neuen Forschungsfeldes auf.

Leseprobe

Table of Contents

Cover

Reviews of Nonlinear Dynamics and Complexity

Title Page

Copyright

List of Contributors

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

References

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

References

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

Acknowledgments

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

Color Plates

Index

End User License Agreement

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Guide

Cover

Table of Contents

Introduction

List of Illustrations

Figure 2.1

Figure 2.2

Figure 2.3

Figure 2.4

Figure 2.5

Figure 2.6

Figure 2.7

Figure 2.8

Figure 2.9

Figure 2.10

Figure 3.1

Figure 3.2

Figure 3.3

Figure 3.4

Figure 3.5

Figure 3.6

Figure 3.7

Figure 4.1

Figure 4.2

Figure 4.3

Figure 4.4

Figure 4.5

Figure 4.6

Figure 5.1

Figure 5.2

Figure 5.3

Figure 5.4

Figure 5.5

Figure 6.1

Figure 6.2

Figure 6.3

Figure 6.4

Figure 6.5

Figure 6.6

Figure 6.7

Figure 6.8

Figure 6.9

Figure 6.10

Figure 6.11

Figure 7.1

Figure 7.2

Figure 7.3

Figure 7.4

Figure 7.5

Figure 7.6

Figure 7.7

Figure 7.8

Figure 7.9

Figure 7.10

Figure 8.1

Figure 8.2

Figure 8.3

Figure 8.4

Figure 8.5

Figure 8.6

Figure 8.7

Figure 9.1

Figure 9.2

Figure 9.3

Figure 9.4

Figure 9.5

Figure 9.6

Figure 9.7

Figure 9.8

Figure 9.9

Figure 10.1

Figure 10.2

Figure 10.3

Figure 10.4

Figure 10.5

Figure 10.6

Figure 10.7

Figure 10.8

Figure 10.9

Figure 10.10

Figure 10.11

Figure 10.12

Figure 10.13

Figure 10.14

Figure 10.15

Figure 10.16

Figure 11.1

Figure 11.2

Figure 11.3

Figure 11.4

Figure 11.5

Figure 12.1

Figure 12.2

Figure 12.3

Figure 12.4

Figure 12.5

Figure 12.6

Figure 12.7

Figure 12.8

Figure 12.9

Figure 13.1

Figure 13.2

Figure 13.3

Figure 13.4

Figure 13.5

Figure 13.6

Figure 13.7

Figure 13.8

Figure 14.1

Figure 14.2

Figure 14.3

Figure 14.4

Figure 15.1

Figure 15.2

Figure 15.3

Figure 15.4

Figure 15.5

Figure 16.1

Figure 16.2

Figure 16.3

Figure 16.4

Figure 16.5

Figure 16.6

Figure 16.7

Figure 17.1

Figure 17.2

Figure 17.3

Figure 17.4

Figure 17.5

Figure 17.6

Figure 17.7

Figure 17.8

Figure 17.9

Figure 18.1

Figure 19.1

Figure 19.2

Figure 19.3

Figure 19.4

Figure 19.5

Figure 20.1

Figure 20.2

Figure 20.3

Figure 20.4

Figure 20.5

Figure 20.6

Figure 20.7

Figure 20.8

Figure 20.9

Figure 20.10

Figure 20.11

Figure 20.12

Figure 21.1

Figure 21.2

Figure 21.3

Figure 21.4

Figure 21.5

Figure 21.6

Figure 21.7

Figure 21.8

Figure 21.9

Figure 21.10

Figure 21.11

Figure 21.12

Figure 21.13

Figure 21.14

Figure 22.1

Figure 22.2

Figure 22.3

Figure 22.4

Figure 22.5

Figure 22.6

Figure 22.7

Figure 22.8

Figure 22.9

Figure 22.10

Figure 22.11

Figure 23.1

Figure 23.2

Figure 23.3

Figure 23.4

Figure 23.5

Figure 23.6

Figure 23.7

Figure 23.8

Figure 24.1

Figure 24.2

Figure 24.3

Figure 24.4

Figure 24.5

Figure 24.6

Figure 24.7

Figure 24.8

List of Tables

Table 9.1

Table 14.1

Table 17.1

Reviews of Nonlinear Dynamics and Complexity

Schuster, H. G. (ed.)

Reviews of Nonlinear Dynamics and Complexity

Volume 1

2008

ISBN: 978-3-527-40729-3

Schuster, H. G. (ed.)

Reviews of Nonlinear Dynamics and Complexity

Volume 2

2009

ISBN: 978-3-527-40850-4

Schuster, H. G. (ed.)

Reviews of Nonlinear Dynamics and Complexity

Volume 3

2010

ISBN: 978-3-527-40945-7

Grigoriev, R. and Schuster, H.G. (eds.)

Transport and Mixing in Laminar Flows

From Microfluidics to Oceanic Currents

2011

ISBN: 978-3-527-41011-8

Lüdge, K. (ed.)

Nonlinear Laser Dynamics

From Quantum Dots to Cryptography

2011

ISBN: 978-3-527-41100-9

Klages, R., Just, W., Jarzynski (eds.)

Nonequilibrium Statistical Physics of Small Systems

Fluctuation Relations and Beyond

2013

ISBN: 978-3-527-41094-1

Plenz, D., Niebur, E., Schuster, H.G. (eds.)

Criticality in Neural Systems

2014

ISBN: 978-3-527-41104-7

Pesenson, M. M. (ed.)

Multiscale Analysis and Nonlinear Dynamics

From Genes to the Brain

2013

ISBN: 978-3-527-41198-6

Criticality in Neural Systems

Edited by

Dietmar Plenz and Ernst Niebur

The Editors

Sr. Invest. Dr. Dietmar Plenz

Nat. Inst. of Mental Health

Systems Neuroscience

Sect. Critical Brain Dynamics

Bethesda, USA

Prof. Dr. Ernst Niebur

The Zanvyl Krieger Mind/ Brain

Inst./John Hopkins University

Baltimore, USA

A book of the series ‘Reviews of Nonlinear Dynamics and Complexity’

The Series Editor

Prof. Dr. Heinz Georg Schuster

Saarbrücken, Germany

Cover Picture

Neuronal avalanches in the awake brain a power law in sizes that grows with the area of cortex observed, a hallmark of criticality (see chapter 02 for details).

Background: Cultured pyramidal neurons from the mammalian brain expressing a genetically encoded calcium indicator to study neuronal avalanches.

From Plenz, NIMH.

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List of Contributors

Lucilla de Arcangelis

Second University of Naples

Department of Industrial and Information Engineering & CNISM

81031 Aversa, CE

Italy

Stefan Bornholdt

Universität Bremen

Institut für Theoretische Physik

Hochschulring 18

28359 Bremen

Germany

Michael Breakspear

Queensland Institute of Medical Research, Royal Brisbane Hospital

Brisbane, QLD 4029

Australia

Dante R. Chialvo

David Geffen School of Medicine

UCLA

Department of Physiology

1500 Wilshire Boulevard

Bldg, 115

Los Angeles, CA 90017

USA

and

Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)

Rivadavia 1917

1033 Buenos Aires

Argentina

Mauro Copelli

Federal University of Pernambuco (UFPE)

Physics Department

50670-901 Recife-PE

Brazil

Richard Coppola

National Institute of Mental Health

MEG Core Facility

10 Center Drive

Bethesda, MD 20892

USA

Jack D. Cowan

University of Chicago

Dept of Mathematics

5734 S. University Ave.

Chicago, IL 60637

USA

Gustavo Deco

Universitat Pompeu Fabra

Theoretical and Computational Neuroscience Center for Brain and Cognition

Roc Boronat 138

08018 Barcelona

Spain

Anne-Ly Do

Max-Planck Institute for the Physics of Complex Systems

Nöthnitzer Str. 38

01187 Dresden

Germany

Wim van Drongelen

University of Chicago

Departments of Pediatrics and Neurology

KCBD 4124

900 E. 57th St.

Chicago, IL 60637

USA

Felix Droste

Bernstein Center for Computational Neuroscience

Haus 2

Philippstrasse 13