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Models of Field Potentials, Slow Waves, and the EEG

  • Ronald J. MacGregor
  • Edwin R. Lewis

Abstract

By far, the larger portion of the book thus far has been concerned with modeling studies which can be related to electrical recordings attributable to single neurons. There exists, however, another class of recordings which measure continuous, graded and more slowly fluctuating waves of activity thought to represent the combined activity of large numbers of neurons or of large regions of neuronal tissue. Such recordings are usually made with macroelectrodes inbedded within nervous tissue, or on the surface of the skull or brain. Even though such recordings were first reported 45 years ago by Berger, contemporary understanding of their physiological basis is remarkedly undeveloped. It is generally assumed that these slow waves are somehow associated with the conglomerate of extracellular field potentials arising from generator potentials in the vast numbers of neurons and glial cells in the general regions recorded from. It is not clear if the characteristic signals of the EEG are determined by physiological properties of individual units, by characteristic patterns of interconnection within local neuronal networks, by interregional circuits, by glial cells, or by some combination of these. Moreover, it is not clear whether or not these fields in turn modulate and influence the activity of neighboring cells such that a truly adequate and comprehensive model would have to include interaction between two distinct but interrelated models of electrical signaling. Moreover, the relationship of unit activity or even multiple unit activity to characteristic features of the EEG has not been established for any significant situation. Quantitative models for any of these processes (which are as yet only understood in outline if at all qualitatively) are virtually nonexistent.

Keywords

Field Potential Slow Wave Solid Angle Parallel Fiber Volume Conductor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Plenum Press, New York 1977

Authors and Affiliations

  • Ronald J. MacGregor
    • 1
  • Edwin R. Lewis
    • 2
  1. 1.University of ColoradoBoulderUSA
  2. 2.University of CaliforniaBerkeleyUSA

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