Detection and Quantification of Correlations in Neural Populations by Coherence Analysis

  • Constantinos N. Christakos


The behavior of the unit-to-aggregate and the aggregate-to-aggregate coherence function as tools for detection and quantification of synchrony in neural populations showing partial correlations is examined by mathematical analysis and computer simulations. The results indicate that the former function provides a suitable tool for both purposes, whereas the latter function is best suited for detection of population synchrony.


Motor Unit Recurrent Laryngeal Nerve Spike Train Coherence Function Neural Population 
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  1. Bullock, T. H. and McClune, M. C., 1989, Lateral coherence of the electrocorticogram: a new measure of brain synchrony, Electroenceph. clin. Neurophysiol. 73: 479–498.CrossRefGoogle Scholar
  2. Christakos, C. N., 1982, A linear stochastic model of the single motor unit, Biol. Cybern. 44: 79–89.MathSciNetzbMATHCrossRefGoogle Scholar
  3. Christakos, C. N.,1990, Modeling aggregate rhythms in neural populations: The stochastic approach, Trends Biol. Cybern. 1: 271–280.Google Scholar
  4. Christakos, C. N., 1994a, Analysis of synchrony (correlations) in neural populations by means of unit-to-aggregate coherence computations, Neurosci. 58: 43–57.CrossRefGoogle Scholar
  5. Christakos, C. N., 1994b, Quantification of synchrony in neural populations by coherence analysis, Soc. Neurosci. Abstr: 20: 1202.Google Scholar
  6. Christakos, C. N., Cohen, M. L, Barnhardt, R. and Shaw. C.-F.. 1991, Fast rhythms in the discharges of phrenic motoneurons and nerves, J. Neurophvsiol. 66: 674–687.Google Scholar
  7. Christakos, C. N.. Cohen. M. I., Sica, A. L., Huang, W.-X., See, W. R. and Barnhardt. R.. 1994, Analysis of recurrent laryngeal inspiratory discharges in relation to fast rhythms, J. Netu•ophysiol. 72: 1304–1316.Google Scholar
  8. Cohen, M. I., 1973, Synchronization of discharge, spontaneous and evoked. between inspiratory neurons, Acta Neurobiol. Exp. 33: 189–218.Google Scholar
  9. Cohen, M. 1.. Christakos, C. N., Barnhardt, R.. Huang. W.-X. and See. W. R.. 1992, State-dependent synchronized fast rhythms in neural networks (inspiratory and sympathetic). Soc. Neurosci. Abstr. 18: 317.Google Scholar
  10. Elul, R., 1972a, The genesis of the EEG, Int. Rev. Neumbiol. 15: 227–272.CrossRefGoogle Scholar
  11. Elul, R., 1972b, Randomness and synchrony in the generation of the electroencephalogram. In: Synchronization of EEG Activity in Epilepsies, Petsche, M. and Brazier, M. A. B. (eds.). Springer. New York, pp. 59–77.CrossRefGoogle Scholar
  12. Houk, J. C., Dessem, D. A., Miller, L. E. and Sybirska, E. H., 1987, Correlation and spectral analysis of relations between single unit discharge and muscle activities, J. _Neurosci. Meth. 21: 201–224.CrossRefGoogle Scholar
  13. Iyer, M. B., Christakos, C. N. and Ghez, C., 1994, Coherent modulations of human motor unit discharges during quasi-sinusoidal isometric muscle contractions, Neurosci. Lett: 170: 94–98.CrossRefGoogle Scholar
  14. Lopes da Silva, F. H., Hoeks, A., Smits, H. and Zetterberg, L. H., 1974, Model of brain rhythmic activity. The alpha rhythm of the thalamus, Kybernetik 15:27–37.Google Scholar
  15. Mitchell, R. A. and Herbert, D. A., 1974, Synchronized high frequency synaptic potentials in medullary respiratory neurons, Brain Res. 75:350–355.Google Scholar
  16. Papoulis, A., 1965, Probability Random Variables and Stochastic Processes,McGraw-Hill_ New York.Google Scholar
  17. Perkel, D. H., Gerstein, G. L. and Moore, G. P., 1967, Neuronal spike trains and point processes. II. Simultaneous spike trains, Biophys. J. 7: 419–440.CrossRefGoogle Scholar
  18. Richardson, C. A. and Mitchell, R. A., 1982. Power spectral analysis of inspiratory nerve activity in the decerebrate cat. Brain Res. 233: 317–336.CrossRefGoogle Scholar
  19. Rosenberg, J. R., Amjad, A. M.. Breeze, P.. Brillinger, D. R. and Halliday. D. M.. 1989. The Fourier approach to the identification of functional coupling between neuronal spike trains, Pro,y. Biophys. Molec. Biol. 53: 1–31.Google Scholar

Copyright information

© Springer Science+Business Media New York 1996

Authors and Affiliations

  • Constantinos N. Christakos
    • 1
    • 2
  1. 1.Department of Basic Sciences, Medical SchoolUniversity of CreteHeraklionGreece
  2. 2.Center for Neurobiology and Behavior College of Physicians and SurgeonsColumbia UniversityNew YorkUSA

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