Cognitive phase transitions in the cerebral cortex : enhancing the neuron doctrine by modeling neural fields /

Cognitive phase transitions in the cerebral cortex : enhancing the neuron doctrine by modeling neural fields /

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Bibliographic Details
Author / Creator:Kozma, Robert, author.
Imprint:Cham : Springer, 2016.
Description:1 online resource : illustrations
Language:English
Series:Studies in systems, decision and control ; 39
Studies in systems, decision and control ; 39.
Subject:
Cognitive neuroscience.
Cognition.
Consciousness.
Neurosciences.
Cognition.
Consciousness.
Cognition.
Cognitive neuroscience.
Consciousness.
Electronic books.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11249573
Hidden Bibliographic Details
Other authors / contributors:Freeman, Walter J., author.
ISBN:9783319244068
331924406X
9783319244044
3319244043
9783319244044
Digital file characteristics:text file PDF
Notes:Includes bibliographical references and index.
English.
Online resource; title from PDF title page (EBSCO, viewed November 4, 2015).
Summary: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.
Other form:Printed edition: 9783319244044
Standard no.:10.1007/978-3-319-24406-8
Table of Contents:
  • Preface; Acknowledgments; Commentators; Contents; Part I Review of Dynamical Brain Theoriesand Experiments; 1 Introduction
  • On the Languages of Brains; 1.1 Brains Are Not Computers; 1.2 Symbolic Approaches to Brains; 1.3 Connectionism; 1.4 Brains as Transient Dynamical Systems; 1.5 Random Graph Theory (RGT) for Brain Models; 1.6 Neuropercolation Modeling Paradigm; References; 2 Experimental Investigation of High-Resolution Spatio-Temporal Patterns; 2.1 Method; 2.1.1 Experiments with Rabbits; 2.1.2 Human ECoG Experiments; 2.1.3 Scalp EEG Design Considerations.
  • 2.2 Temporal Patterns: The Carrier Wave2.3 Spatial Patterns of Amplitude Modulation (AM) and Phase Modulation (PM); 2.4 Classification of ECoG and EEG AM Patterns; 2.5 Characterization of Synchronization-Desynchronization Transitions in the Cortex; 2.6 Experimental Observation of Singularity; 2.7 Transmission of Macroscopic Output by Microscopic Pulses; References; 3 Interpretation of Experimental Results As Cortical Phase Transitions; 3.1 Theoretical Approaches to Nonlinear Cortical Dynamics; 3.2 Scales of Representation: Micro-, Meso-, and Macroscopic Levels.
  • 3.3 Cinematic Theory of Cortical Phase Transitions3.4 Characterization of Phase Transitions; 3.4.1 Critical State; 3.4.2 Singular Dynamics; 3.4.3 Symmetry Breaking; 3.4.4 Transition Energy; 3.4.5 Zero Order Parameter; 3.4.6 Correlation Length Divergence; References; 4 Short and Long Edges in Random Graphs for Neuropil Modeling; 4.1 Motivation of Using Random Graph Theory for Modeling Cortical Processes; 4.2 Glossary of Random Graph Terminology; 4.3 Neuropercolation Basics; 4.4 Critical Behavior in Neuropercolation with Mean-Field, Local, and Mixed Models; 4.4.1 Mean-Field Approximation.
  • 4.4.2 Mixed Short and Long Connections4.5 Finite Size Scaling Theory of Criticality in Brain Models ; References; 5 Critical Behavior in Hierarchical Neuropercolation Models of Cognition; 5.1 Basic Principles of Hierarchical Brain Models; 5.2 Narrow-Band Oscillations in Lattices with Inhibitory Feedback; 5.3 Broad-Band Oscillations in Coupled Multiple Excitatory-Inhibitory Layers; 5.4 Exponentially Expanding Graph Model; References; 6 Modeling Cortical Phase Transitions Using Random Graph Theory; 6.1 Describing Brain Networks in Terms of Graph Theory.
  • 6.1.1 Synchronization and the 'Aha' Moment6.1.2 Practical Considerations on Synchrony; 6.1.3 Results of Synchronization Measurements; 6.2 Evolution of Critical Behavior in the Neuropil
  • a Hypothesis; 6.3 Singularity and sudden transitions
  • Interpretation of Experimental Findings; References; 7 Summary of Main Arguments; 7.1 Brain Imaging Combining Structural and Functional MRI, EEG, MEG and Unit Recordings; 7.2 Significance of RGT for Brain Modeling; 7.2.1 Relevance to Brain Diseases; 7.2.2 Neuropercolation as a Novel Mathematical Tool; 7.3 Neuromorphic Nanoscale Hardware Platforms.

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