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  • 1
    UID:
    almahu_9949419112502882
    Format: XXX, 306 p. 103 illus., 24 illus. in color. , online resource.
    Edition: 1st ed. 2010.
    ISBN: 9781441907967
    Series Statement: Springer Series in Computational Neuroscience, 4
    Content: Foreword by Walter J. Freeman. The induction of unconsciousness using anesthetic drugs demonstrates that the cerebral cortex can operate in two very different modes: alert and responsive versus unaware and quiescent. But the states of wakefulness and sleep are not single-neuron properties---they emerge as bulk properties of cooperating populations of neurons, with the switchover between states being similar to the physical change of phase observed when water freezes or ice melts. Some brain-state transitions, such as sleep cycling, anesthetic induction, epileptic seizure, are obvious and detected readily with a few EEG electrodes; others, such as the emergence of gamma rhythms during cognition, or the ultra-slow BOLD rhythms of relaxed free-association, are much more subtle. The unifying theme of this book is the notion that all of these bulk changes in brain behavior can be treated as phase transitions between distinct brain states. "Modeling Phase Transitions in the Brain" contains chapter contributions from leading researchers who apply state-space methods, network models, and biophysically-motivated continuum approaches to investigate a range of neuroscientifically relevant problems that include analysis of nonstationary EEG time-series; network topologies that limit epileptic spreading; saddle--node bifurcations for anesthesia, sleep-cycling, and the wake--sleep switch; prediction of dynamical and noise-induced spatiotemporal instabilities underlying BOLD, alpha-, and gamma-band EEG oscillations, gap-junction-moderated Turing structures, and Hopf--Turing interactions leading to cortical waves. Written for: Researchers, clinicians, physicians, neurologists About the editors: Alistair Steyn-Ross and Moira Steyn-Ross are computational and theoretical physicists in the Department of Engineering, University of Waikato, New Zealand. They share a long-standing interest in the application of physics-based methods to gain insight into the emergent behavior of complex biological systems such as single neurons and interacting neural populations.
    Note: Phase transitions in single neurons and neural populations: Critical slowing, anesthesia, and sleep cycles -- Generalized state-space models for modeling nonstationary EEG time-series -- Spatiotemporal instabilities in neural fields and the effects of additive noise -- Spontaneous brain dynamics emerges at the edge of instability -- Limited spreading: How hierarchical networks prevent the transition to the epileptic state -- Bifurcations and state changes in the human alpha rhythm: Theory and experiment -- Inducing transitions in mesoscopic brain dynamics -- Phase transitions in physiologically-based multiscale mean-field brain models -- A continuum model for the dynamics of the phase transition from slow-wave sleep to REM sleep -- What can a mean-field model tell us about the dynamics of the cortex? -- Phase transitions, cortical gamma, and the selection and read-out of information stored in synapses -- Cortical patterns and gamma genesis are modulated by reversal potentials and gap-junction diffusion.
    In: Springer Nature eBook
    Additional Edition: Printed edition: ISBN 9781441907974
    Additional Edition: Printed edition: ISBN 9781441907950
    Additional Edition: Printed edition: ISBN 9781461425502
    Language: English
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  • 2
    Online Resource
    Online Resource
    New York, NY : Springer Science+Business Media, LLC
    UID:
    gbv_62477192X
    Format: Online-Ressource , v.: digital
    Edition: Online-Ausg. Springer eBook Collection. Biomedical and Life Sciences Electronic reproduction; Available via World Wide Web
    ISBN: 9781441907967
    Series Statement: Springer Series in Computational Neuroscience 4
    Content: Foreword by Walter J. Freeman. The induction of unconsciousness using anesthetic agents demonstrates that the cerebral cortex can operate in two very different behavioral modes: alert and responsive vs. unaware and quiescent. But the states of wakefulness and sleep are not single-neuron properties---they emerge as bulk properties of cooperating populations of neurons, with the switchover between states being similar to the physical change of phase observed when water freezes or ice melts. Some brain-state transitions, such as sleep cycling, anesthetic induction, epileptic seizure, are obvious
    Note: Includes bibliographical references and index , Foreword; List of Contributors; Acronyms; Contents; Introduction; 1 Phase transitions in single neurons and neural populations: Critical slowing, anesthesia, and sleep cycles; D.A. Steyn-Ross, M.L. Steyn-Ross, M.T. Wilson, and J.W. Sleigh; 1.1 Introduction; 1.2 Phase transitions in single neurons; 1.2.1 H.R. Wilson spiking neuron model; 1.2.2 Type-I and type-II subthreshold fluctuations; 1.2.3 Theoretical fluctuation statistics for approachto criticality; 1.2.3.1 Fluctuation variance; 1.2.3.2 Fluctuation spectrum; 1.3 The anesthesia state; 1.3.1 Effect of anesthetics on bioluminescence , 1.3.2 Effect of propofol anesthetic on EEG1.4 SWS--REM sleep transition; 1.4.1 Modeling the SWS--REM sleep transition; 1.5 The hypnic jerk and the wake--sleep transition; 1.6 Discussion; References; 2 Generalized state-space models for modeling nonstationary EEG time-series; A. Galka, K.K.F. Wong, and T. Ozaki; 2.1 Introduction; 2.2 Innovation approach to time-series modeling; 2.3 Maximum-likelihood estimation of parameters; 2.4 State-space modeling; 2.4.1 State-space representation of ARMA models; 2.4.2 Modal representation of state-space models , 2.4.3 The dynamics of AR(1) and ARMA(2,1) processes2.4.4 State-space models with component structure; 2.5 State-space GARCH modeling; 2.5.1 State prediction error estimate; 2.5.2 State-space GARCH dynamical equation; 2.5.3 Interface to Kalman filtering; 2.5.4 Some remarks on practical model fitting; 2.6 Application examples; 2.6.1 Transition to anesthesia; 2.6.2 Sleep stage transition; 2.6.3 Temporal-lobe epilepsy; 2.7 Discussion and summary; References; 3 Spatiotemporal instabilities in neural fieldsand the effects of additive noise; Axel Hutt; 3.1 Introduction; 3.1.1 The basic model , 3.1.2 Model properties and the extended model3.2 Linear stability in the deterministic system; 3.2.1 Specific model; 3.2.2 Stationary (Turing) instability; 3.2.3 Oscillatory instability; 3.3 External noise; 3.3.1 Stochastic stability; 3.3.2 Noise-induced critical fluctuations; 3.4 Nonlinear analysis of the Turing instability; 3.4.1 Deterministic analysis; 3.4.2 Stochastic analysis at order O(3/2); 3.4.3 Stochastic analysis at order O(5/2); 3.5 Conclusion; References; 4 Spontaneous brain dynamics emerges at the edge of instability; V.K. Jirsa and A. Ghosh; 4.1 Introduction , 4.2 Concept of instability, noise, and dynamic repertoire4.3 Exploration of the brain's instabilities during rest; 4.4 Dynamical invariants of the human resting-state EEG; 4.4.1 Time-series analysis; 4.4.2 Spatiotemporal analysis; 4.5 Final remarks; References; 5 Limited spreading: How hierarchical networks prevent the transition to the epileptic state; M. Kaiser J. Simonotto; 5.1 Introduction; 5.1.1 Self-organized criticality and avalanches; 5.1.2 Epilepsy as large-scale critical synchronized event; 5.1.3 Hierarchical cluster organization of neural systems , 5.2 Phase transition to the epileptic state , Electronic reproduction; Available via World Wide Web
    Additional Edition: ISBN 9781441907950
    Language: English
    URL: Volltext  (lizenzpflichtig)
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  • 3
    Book
    Book
    New York ; Dordrecht ; Heidelberg ; London :Springer,
    UID:
    almafu_BV043658569
    Format: xxix, 305 Seiten : , Illustrationen, Diagramme.
    ISBN: 978-1-441-90795-0
    Series Statement: Springer series in computational neuroscience volume 4
    Note: Includes bibliographical references and index
    Additional Edition: Erscheint auch als Online-Ausgabe ISBN 978-1-4419-0796-7
    Language: English
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