Neuronal Avalanches in Cortical Networks
Dietmar Plenz
The Dynamic Brain in Action: Coordinative Structures,
Criticality and Coordination Dynamics
J. A. Scott Kelso
Critical Brain Dynamics at Large Scale
Dante R Chialvo
The Correlation of the Neuronal Long-range Temporal Correlations,
Avalanche Dynamics with the Behavioral Scaling Laws and
Interindividual Variability
J. Matias Palva and Satu Palva
The Turbulent Human Brain
Arnold. J. Mandell, Stephen E. Robinson, Karen A. Selz, Constance
Schrader, Tom Holroyd and Richard Coppola
Thermodynamic Model of Criticality in the Cortex Based on EEG/ECoG
Data
Robert Kozma, Marko Puljic and Walter J. Freeman
Neuronal Avalanches in the Human Brain
Oren Shriki and Dietmar Plenz
Critical Slowing and Perception
Karl Friston, Michael Breakspear and Gustavo Deco
Self-organized Criticality in Neural Network Models
Matthias Rybarsch and Stefan Bornholdt
Single Neuron Response Fluctuations: A Self-organized Criticality
Point of View
Asaf Gal and Shimon Marom
Activity Dependent Model for Neuronal Avalanches
Lucilla de Arcangelis and Hans J. Herrmann
The Neuronal Network Oscillation as a Critical Phenomenon
Richard Hardstone, Huibert D. Mansvelder, Klaus
Linkenkaer-Hansen
Critical Exponents, Universality Class & Thermodynamic: The
"Temperature" Of the Brain
Shan Yu, Hongdian Yang, Oren Shriki and Dietmar Plenz
Peak Variability and Optimal Performance in Cortical Networks at
Criticality
Hongdian Yang, Woodrow L. Shew, Rajarshy Roy, and Dietmar Plenz
Criticality At Work: How Do Critical Networks Respond to
Stimuli?
Mauro Copelli
Critical Dynamics in Complex Networks
Daniel B. Larremore, Woodrow L. Shew, Juan G. Restrepo
Mechanisms of Self-organized Criticality in Adaptive Networks
Thilo Gross, Anne-Ly Do, Felix Droste, and Christian Meisel
Cortical Networks with Lognormal Synaptic Connectivity and their
Implications in Neuronal Avalanches
Tomoki Fukai, Vladimir Klinshov and Jun-nosuke Teramae
Jump Right In: Transitions to Criticality in Neural Systems with
Dynamic Synapses
Anna Levina, J. Michael Herrmann, Theo Geisel
Non-conservative Neuronal Networks During Up states Self-organize
Near Critical Points
Stefan Mihalas, Daniel Millman, Ramakrishnan Iyer, Alfredo Kirkwood
and Ernst Niebur
Self-organized Criticality and Near Criticality in Neural
Networks
J. D. Cowan, J. Neuman, and W. van Drongelen
Neural Dynamics: Criticality, Cooperation, Avalanches and
Entrainment between Complex Networks
P. Grigolini, M. Zare, A. Svenkeson, B. J. West
Complex Networks: From Social Crises to Neuronal Avalanches
B. J. West, M. Turalska and P. Grigolini
The Dynamics of Neuromodulation
Gerhard Werner and Bernhard J. Mitterauer
DIETMAR PLENZ is Chief of the Section on Critical BrainDynamics in the Intramural Research Program at the NationalInstitute of Mental Health. He received his Ph.D. in 1993 at theMax-Planck Institute of Biological Cybernetics and the UniversityTuebingen. Dr. Plenz joined the NIMH as an Investigator in 1999. Hepioneered the development of in vitro networks to study andidentify the emergence of neuronal avalanches in the brain. ERNST NIEBUR is Professor of Neuroscience and of Brainand Psychological Sciences at Johns Hopkins University inBaltimore, USA. He holds degrees in Physics from the Universitiesof Dortmund, Germany and Lausanne, Switzerland, and apostgraduate certificate in Artificial Intelligence from the SwissFederal Institute of Technology (EPFL). Prof. Niebur has authoredmore than 100 scientific articles in physics and computationalneuroscience. HEINZ GEORG SCHUSTER is Professor (em.) of TheoreticalPhysics at the University of Kiel in Germany. At the beginning ofhis academic career, he was appointed Professor at the Universityof Frankfurt am Main in Germany. He was a visiting professor at theWeizmann-Institute of Science in Israel and at the CaliforniaInstitute of Technology in Pasadena, USA. He is author and editorof research monographs and topical handbooks on chaos theory,nonlinear dynamics and neural networks, but also on popular sciencebooks, and editor of a Wiley series on Nonlinear Physics andComplexity.
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