Neuroscience and Behavioral Physiology

, Volume 27, Issue 4, pp 381–390 | Cite as

Dynamics of high-frequency (up to 200 Hz) components of brain electrical activity during learning reflect the functional mosaicism of the neocortex

  • V. N. Dumenko
  • M. K. Kozlov
  • M. A. Kulikov


This study was undertaken with the aim of identifying frequency bands with correlated changes in the spectral power amplitudes of brain electrical activity, including high-frequency components (the 1–200 Hz band) in four dogs, using one-dimensional analysis. Factor and cluster analysis of the spectral densities of various parts of the cortex and the olfactory bulb were carried out. The ratios of factors in different parts of the brain, both in terms of the proportions of the total dispersity and in terms of weightings, provided data on regional and individual differences in electrical activity. During learning (development of a motor habit consisting of pressing a feeder pedal), the factor organization of electrical activity became more complex, particularly in the high-frequency part of the spectrum (40–170 Hz). The changes consisted of the appearance of narrower frequency sub-bands, each of which was present at high weighting (0.7–0.9) for one of the factors. The use of high-frequency components allowed functional mosaicism of the neocortex to be detected.


Electrical Activity Olfactory Bulb Factor Organization Spectral Density Function Alpha Rhythm 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    O. A. Adrianov and T. A. Mering, Atlas of the Dog Brain [in Russian], Medgiz, Moscow (1959).Google Scholar
  2. 2.
    A. Afifi and S. Eisen, Statistical Analysis. A Computer Approach [Russian translation], Mir, Moscow (1982).Google Scholar
  3. 3.
    V. N. Dumenko, Learning and the High-Frequency Components of Brain Electrical Activity [in Russian], Nauka, Moscow (1992).Google Scholar
  4. 4.
    V. N. Dumenko, M. K. Kozlov, and M. A. Kulikov, “Attempts to discriminate the high-frequency range (40–200 Hz) of brain electrical activity in the dog into frequency bands,” Zh. Vyssh. Nerv. Deyat.,45, No. 1, 107 (1995).Google Scholar
  5. 5.
    V. N. Dumenko, M. A. Kulikov, and M. K. Kozlov, “Is the gamma band of brain electrical activity homogeneous?,” Dokl. Akad. Nauk,339, No. 1, 120 (1994).PubMedGoogle Scholar
  6. 6.
    K. Iberla, Factor Analysis [in Russian], Statistika, Moscow (1980).Google Scholar
  7. 7.
    M. K. Kozlov, “Construction of a density profile of the one-dimensional distribution as a cluster approach to the problem of EEG classification,” Zh. Vyssh. Nerv. Deyat.,44, No. 1, 175 (1994).Google Scholar
  8. 8.
    M. K. Kozlov, “A version of cluster analysis which eliminates the cluster as a vague concept,” Dokl. Akad. Nauk,348, No. 1, 34 (1996).Google Scholar
  9. 9.
    M. N. Livanov, The Spatial Organization of Brain Processes [in Russian], Nauka, Moscow (1972).Google Scholar
  10. 10.
    G. Khardman, Current Factor Analysis [in Russian], Statistika, Moscow (1972).Google Scholar
  11. 11.
    S. L. Bressler, “Relation of olfactory bulb and cortex,” Brain Res.,409, No. 2, 285 (1987).PubMedCrossRefGoogle Scholar
  12. 12.
    S. L. Bressler and W. Y. Freeman, “Frequency analysis of olfactory system EEG in cat, rabbit and rat,” EEG Clin. Neurophysiol.,50, No. 1, 19 (1980).CrossRefGoogle Scholar
  13. 13.
    W. Y. Freeman and W. Schneider, “Changes in spatial patterns of rabbit olfactory EEG with conditioning to odors,” Psychophysiol.,19, No. 1, 44 (1982).Google Scholar

Copyright information

© Plenum Publishing Corporation 1997

Authors and Affiliations

  • V. N. Dumenko
  • M. K. Kozlov
  • M. A. Kulikov

There are no affiliations available

Personalised recommendations