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Biological Cybernetics

, Volume 64, Issue 1, pp 51–60 | Cite as

A sensitive estimator for crosscorrelograms

  • L. Nelken
  • E. Vaadia
Article

Abstract

The best established method for finding interactions between extracellularly recorded neurons is the crosscorrelation technique. The method is simple and useful, but it has some drawbacks. One of them is its limited sensitivity to weak interactions, which are common in the mammalian cerebral cortex. In the present paper a new method for the estimation of interaction strength is presented. This method is based on the intensity representation of point processes, and provides an optimal estimator for the intensity of the postsynaptic spike train. The estimator is complicated to use, but it can be approximated by a simple estimator, similar to ordinary measures of synaptic efficacy like the area under the crosscorrelogram peak. Simulation results, showing the advantage of the new estimator over the commonly used efficacy estimators and some measure of its robustness to deviations from model assumptions, are presented. Finally, application of the estimator to the analysis of simultaneous recordings of physiological single units is demonstrated.

Keywords

Cerebral Cortex Crosscorrelation Point Process Single Unit Spike Train 
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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References

  1. Abeles M (1982a) Quantification, smoothing and confidence limits for single units' histograms. J Neurosci Methods 5:317–325CrossRefGoogle Scholar
  2. Abeles M (1982b) Local cortical circuits: an electrophysiological study. Springer, Berlin Heidelberg New YorkGoogle Scholar
  3. Abeles M (1990) Corticonics: neural networks of the cerebral cortex. Cambridge University Press, Cambridge: (to be published)Google Scholar
  4. Aertsen AMHJ, Gerstein GL (1985) Evaluation of neuronal connectivity: sensitivity of cross-correlation. Brain Res 340:341–354CrossRefPubMedGoogle Scholar
  5. Aertsen AMHJ, Gerstein GL, Habib MK, Palm G (1989) Dynamics of neuronal firing correlation: Modulation of “effective connectivity”. J Neurophysiol 61:900–917PubMedGoogle Scholar
  6. Borisyuk GN, Borisyuk RM, Kirillov AB, Kovalenko EI, Kryukov VI (1985) A new statistical method for identifying interconnections between neuronal network elements. Biol Cybern 52:301–306CrossRefPubMedGoogle Scholar
  7. Bremaud P (1981) Point processes and queues: martingale dynamics. Springer, New York Berlin HeidelbergGoogle Scholar
  8. Burns DB, Webb AC (1979) The correlation between discharge times of neighboring neurons in isolated cerebral cortex. Proc R Soc Lond B 203:347–360PubMedGoogle Scholar
  9. Chornoby, ES, Schramm, LP, Karr, AF (1988) Maximum likelihood identification of neural point proces systems. Biol Cybern 59:265–275CrossRefPubMedGoogle Scholar
  10. Espinosa IE, Gerstein GL (1988) Cortical auditory neurons interactions during presentation of 3-tones sequences: effective connectivity. Brain Res 450:39–50CrossRefPubMedGoogle Scholar
  11. Fetz EE, Gustafson B (1983) Relation between shapes of postsynaptic potentials and changes in firing probability of cat motorneurons. J Physiol (Lond) 341:387–410Google Scholar
  12. Frostig RD, Frostig Z, Harper RM (1984) Information trains: The technique and its uses in spike train and network analysis, with examples taken from the nucleus parabrachialis medialis during sleep-waking states. Brain Res 322:76–74CrossRefGoogle Scholar
  13. Gerstein GL (1970) Functional association of neurons: Detection and interpretation. In: Schmitt FO (ed) The neurosciences: second study program. Rockefeller University Press, New YorkGoogle Scholar
  14. Jacobsen M (1982) Statistical analysis of counting processes. Lecture Notes in Statistics Vol 12. Springer New York Berlin HeidelbergGoogle Scholar
  15. Kirkwood PA, Sears TA (1982) The effect of single afferent impulses on the probability of firing of external intercostal motorneurons in the cat. J Physiol 322:315–336PubMedGoogle Scholar
  16. Kruger J, Aiple F (1988) Multimicroelectrode investigation of monkey striate cortex: spike train correlations in infragranular layers. J Neurophysiol 60:798–828PubMedGoogle Scholar
  17. Levick WR, Cleland BG, Dubin MW (1972) Lateral geniculate neurons of cat: retinal inputs and physiology. Invest Ophtalmol 1:302–310Google Scholar
  18. Lindsey BG, Gerstein GL (1979) Interactions among an ensemble of chordotonal organ receptors and motor neurons of the crayfish claw. J Neurophysiol 42:383–399PubMedGoogle Scholar
  19. Melssen WJ, Epping WJ (1987) Detection and estimation of neural connectivity based on crosscorrelation analysis. Biol Cybern 57:403–414CrossRefPubMedGoogle Scholar
  20. Moore GP, Perkel DH, Segundo JP (1966) Statistical analysis and functional interpretation of neuronal spike data. Ann Rev Physiol 28:493–522CrossRefGoogle Scholar
  21. Moore GP, Segundo JP, Perkel DH, Levitan H (1970) Statistical signs of synaptic interactions in neurons. Biophys J 10:876–900PubMedGoogle Scholar
  22. Perkel DH, Gerstein GL, Moore GP (1967) Neuronal spike trains and stochastic point processes: II. Simultaneous spike trains. Biophys J 7:419–440PubMedGoogle Scholar
  23. Singer W, Gray CM, Engel A, Konig P (1989) Spatio-temporal distribution of stimulus-specific oscillations in the cat visual cortex. II: global interactions. Soc Neurosci (abstr) 14:362.13Google Scholar
  24. Ts'o D, Gilbert CD, Wiesel T (1986) Relationships between horizontal architecture in cat striate cortex as revealed by cross-correlation analysis. J Neurosci 6:1160–1170PubMedGoogle Scholar
  25. Vaadia E, Bergman H, Abeles M (1989) Neuronal activities related to higher bran fucntions — Theoretical and experimental implications. IEEE Trans Biomed Eng 36:25–35CrossRefGoogle Scholar
  26. Vaadia E, Ahissar E, Bergman H, Lavner Y (1990) Correlated activity of neurons: a neural code for higher brain functions? In: Kruger J (ed) Neuronal cooperation. Springer, Berlin Heidelberg New York: (to be published)Google Scholar

Copyright information

© Springer-Verlag 1990

Authors and Affiliations

  • L. Nelken
    • 1
  • E. Vaadia
    • 1
  1. 1.Department of PhysiologyHadassah Medical School, The Hebrew UniversityJerusalemIsrael

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