Evaluation of neuronal connectivity: sensitivity of cross-correlation.

Cross-correlation analysis of separable multi-unit activity is one of the most commonly used methods to investigate connectivity in neural networks. In the course of development of new analysis techniques which go beyond the study of pairs or triplets of neurons, the need arose for a simple yet versatile simulator to generate spike trains from networks of specified structure. The present paper describes such a simulator and presents some examples of its performance as analyzed by cross-correlation. We noted a distinct asymmetry in the sensitivity of cross-correlation for the presence of excitatory vs inhibitory connections. A theoretical analysis is given from which quantitative criteria for detectability were derived. It appears that indeed the sensitivity of cross-correlation for excitation is larger to an order of magnitude than it is for inhibition. Possible consequences of this finding are indicated, and the relation to commonly used methods to measure strength of interaction are discussed.