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.
PMID: 4027655 [PubMed - indexed for MEDLINE]
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