This paper illustrates the use of the recently introduced method of partial directed coherence in approaching how interactions among neural structures change over short time spans that characterize well defined behavioral states. Central to the method is its use of multivariate time series modelling in conjunction with the concept of Granger causality. Simulated neural network models were used to illustrate the technique's power and limitations when dealing with neural spiking data. This was followed by the analysis of multi-unit activity data illustrating dynamical change in the interaction of thalamo-cortical structures in a behaving rat.