MEASURE AND STATISTICAL TEST FOR CROSS-CORRELATION BETWEEN PAIRED NEURONAL SPIKE TRAINS WITH SMALL SAMPLE-SIZE

Recent development of multi-unit recording techniques such as optical recording and multi-electrode arrays makes it possible to record neuro nal activities from tens or hundreds of neurons simultaneously. To ana lyze functional connections between these neurons, cross-correlation a nalysis has been most commonly applied to the hundreds to thousands of pairs of these neurons. However, conventional cross-correlation data needs statistical tests for significance especially when the sample si ze of recorded spike trains is small. Here, a multiple hypergeometric model based on a transformation of the cross-correlogram data to a 2 X J table has been suggested. The exact p value for significance can be obtained by the generalized Fisher's method with small sample size an d a cross-correlation coefficient for the strength of cross-correlatio n can be obtained based on the R-square analogue for nominal data. For large sample size, chi(2) test can be applied based on the same trans formation. Examples of real spike train data set and simulation show t hat the methods are applicable to the data of multi-unit activity with only tens of spikes. These methods are especially useful when thousan ds of cross-correlograms need to be screened quickly and automatically .