Abstract
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.
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References
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.
Christakos, C. N., 1982, A linear stochastic model of the single motor unit, Biol. Cybern. 44: 79–89.
Christakos, C. N.,1990, Modeling aggregate rhythms in neural populations: The stochastic approach, Trends Biol. Cybern. 1: 271–280.
Christakos, C. N., 1994a, Analysis of synchrony (correlations) in neural populations by means of unit-to-aggregate coherence computations, Neurosci. 58: 43–57.
Christakos, C. N., 1994b, Quantification of synchrony in neural populations by coherence analysis, Soc. Neurosci. Abstr: 20: 1202.
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.
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.
Cohen, M. I., 1973, Synchronization of discharge, spontaneous and evoked. between inspiratory neurons, Acta Neurobiol. Exp. 33: 189–218.
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.
Elul, R., 1972a, The genesis of the EEG, Int. Rev. Neumbiol. 15: 227–272.
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.
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.
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.
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.
Mitchell, R. A. and Herbert, D. A., 1974, Synchronized high frequency synaptic potentials in medullary respiratory neurons, Brain Res. 75:350–355.
Papoulis, A., 1965, Probability Random Variables and Stochastic Processes,McGraw-Hill_ New York.
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.
Richardson, C. A. and Mitchell, R. A., 1982. Power spectral analysis of inspiratory nerve activity in the decerebrate cat. Brain Res. 233: 317–336.
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.
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Christakos, C.N. (1996). Detection and Quantification of Correlations in Neural Populations by Coherence Analysis. In: Gath, I., Inbar, G.F. (eds) Advances in Processing and Pattern Analysis of Biological Signals. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-9098-6_12
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DOI: https://doi.org/10.1007/978-1-4757-9098-6_12
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