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J Neurophysiol (April 1, 2003). 10.1152/jn.000889.2002
Submitted on Submitted 4 October 2002; accepted in final form 5 December 2002
1Interdepartmental Program for Neuroscience, Brain Research Institute and 2Departments of Neurobiology and Psychology, and Jules Stein Eye Institute, University of California, Los Angeles, California 90095
Dorn, Jessy D. and
Dario
L. Ringach.
Estimating Membrane Voltage Correlations From Extracellular Spike
Trains. J. Neurophysiol. 89: 2271-2278, 2003. The cross-correlation coefficient between neural spike
trains is a commonly used tool in the study of neural interactions. Two
well-known complications that arise in its interpretation are
1) modulations in the correlation coefficient may result
solely from changes in the mean firing rate of the cells and
2) the mean firing rates of the neurons impose upper and
lower bounds on the correlation coefficient whose absolute values
differ by an order of magnitude or more. Here, we propose a model-based
approach to the interpretation of spike train correlations that
circumvents these problems. The basic idea of our proposal is to
estimate the cross-correlation coefficient between the membrane
voltages of two cells from their extracellular spike trains and use the resulting value as the degree of correlation (or association) of neural
activity. This is done in the context of a model that assumes the
membrane voltages of the cells have a joint normal distribution and
spikes are generated by a simple thresholding operation. We show that,
under these assumptions, the estimation of the correlation coefficient
between the membrane voltages reduces to the calculation of a
tetrachoric correlation coefficient (a measure of association in
nominal data introduced by Karl Pearson) on a contingency table
calculated from the spike data. Simulations of conductance-based leaky
integrate-and-fire neurons indicate that, despite its simplicity, the
technique yields very good estimates of the intracellular membrane
voltage correlation from the extracellular spike trains in biologically
realistic models.
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