Spatio-temporal correlations in spike trains of simultaneously recorded neurons characterize stochastic interactions in neural networks. Information theoretic measures for ''spatial'' and ''temporal stochastic interaction'' can measure the total amount of dependence in a set of cells. In the present work we calculate these interaction measures for associative networks, the most prominent models for cortical gamma-oscillations and precisely repetiting spike patterns (synfire chains). Stochastic interaction in these networks appears to be very high conflicting with the common belief that neurons are largely independent Poisson processes.