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Neurodynamics: An Exploration in Mesoscopic Brain Dynamics

  • Book
  • © 2000

Overview

Authors:
  1. Walter J. Freeman

    1. Department of Molecular and Cell Biology, University of California, Berkeley, USA

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Part of the book series: Perspectives in Neural Computing (PERSPECT.NEURAL)

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Table of contents (16 chapters)

  1. Front Matter

    Pages i-ix
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  2. Prolog

    1. Prolog

      • Walter J. Freeman
      Pages 1-24

  3. The dynamics of neural interaction and transmission

    1. Front Matter

      Pages 25-25
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    2. Spatial Mapping of Evoked Brain Potentials and EEGs to Define Population State Variables

      • Walter J. Freeman
      Pages 27-50

    3. Linear Models of Impulse Inputs and Linear Basis Functions for Measuring Impulse Responses

      • Walter J. Freeman
      Pages 51-67

    4. Rational Approximations in the Complex Plane for Laplace Transforms of Transcendental Linear Operators

      • Walter J. Freeman
      Pages 69-89

    5. Root Locus Analysis of Piecewise Linearized Models with Multiple Feedback Loops and Unilateral or Bilateral Saturation

      • Walter J. Freeman
      Pages 91-123

    6. Opening Feedback Loops with Surgery and Anesthesia; Closing the Loops with Noise

      • Walter J. Freeman
      Pages 125-139

    7. Three Degrees of Freedom in Neural Populations: Arousal, Learning, and Bistability

      • Walter J. Freeman
      Pages 141-163

    8. Analog Computation to Model Responses Based on Linear Integration, Modifiable Synapses, and Nonlinear Trigger Zones

      • Walter J. Freeman
      Pages 165-175

    9. Stability Analysis to Derive and Regulate Homeostatic Set Points for Negative Feedback Loops

      • Walter J. Freeman
      Pages 177-208

  4. Designation of Contents as Meaning, not Information

    1. Front Matter

      Pages 209-209
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    2. Multichannel Recording to Reveal the “Code” of the Cortex: Spatial Patterns of Amplitude Modulation (AM) of Mesoscopic Carrier Waves

      • Walter J. Freeman
      Pages 211-239

    3. Relations Between Microscopic and Mesoscopic Levels Shown by Calculating Pulse Probability Conditional on EEG Amplitude, Giving the Asymmetric Sigmoid Function

      • Walter J. Freeman
      Pages 241-264

    4. The Use of Euclidean Distance in 64-space and Behavioral Correlates to Optimize Filters for Gamma AM Pattern Classification

      • Walter J. Freeman
      Pages 265-290

    5. Simulating Gamma Waveforms, AM Patterns and 1/f α Spectra by Means of Mesoscopic Chaotic Neurodynamics

      • Walter J. Freeman
      Pages 291-312

    6. Tuning Curves to Optimize Temporal Segmentation and Parameter Evaluation of Adaptive Filters for Neocortical EEG

      • Walter J. Freeman
      Pages 313-349

    7. Stochastic Differential Equations and Random Number Generators Minimize Numerical Instabilities in Digital Simulations

      • Walter J. Freeman
      Pages 351-367

  5. Epilog: Problems for Further Development in Mesoscopic Brain Dynamics

    1. Links Between Microscopic Neurons and Mesoscopic Neural Assemblies

      • Walter J. Freeman
      Pages 369-376

  6. Back Matter

    Pages 377-397
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Keywords

About this book

Cortical evoked potentials are of interest primarily as tests of changing neuronal excitabilities accompanying normal brain function. The first three steps in the anal­ ysis of these complex waveforms are proper placement of electrodes for recording, the proper choice of electrical or sensory stimulus parameters, and the establish­ ment of behavioral control. The fourth is development of techniques for reliable measurement. Measurement consists of comparison of an unknown entity with a set of standard scales or dimensions having numerical attributes in preassigned degree. A physical object can be described by the dimensions of size, mass, density, etc. In addition there are dimensions such as location, velocity, weight, hardness, etc. Some of these dimensions can be complex (e. g. size depends on three or more subsidiary coordi­ nates), and some can be interdependent or nonorthogonal (e. g. specification of size and mass may determine density). In each dimension the unit is defined with refer­ ence to a standard physical entity, e. g. a unit of mass or length, and the result of measurement is expressed as an equivalence between the unknown and the sum of a specified number of units of that entity. The dimensions of a complex waveform are elementary waveforms from which that waveform can be built by simple addition. Any finite single-valued function of time is admissible. They are called basis functions (lO, 15), and they can be expressed in numeric as well as geometric form.

Authors and Affiliations

  • Department of Molecular and Cell Biology, University of California, Berkeley, USA

    Walter J. Freeman

Bibliographic Information

  • Book Title: Neurodynamics: An Exploration in Mesoscopic Brain Dynamics

  • Authors: Walter J. Freeman

  • Series Title: Perspectives in Neural Computing

  • DOI: https://doi.org/10.1007/978-1-4471-0371-4

  • Publisher: Springer London

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer-Verlag London 2000

  • Softcover ISBN: 978-1-85233-616-5Published: 01 March 2000

  • eBook ISBN: 978-1-4471-0371-4Published: 06 December 2012

  • Series ISSN: 1431-6854

  • Edition Number: 1

  • Number of Pages: X, 398

  • Number of Illustrations: 32 b/w illustrations

  • Topics: Neurosciences, Artificial Intelligence, Pattern Recognition, Neurology

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