This study proposes a technique for determining effective connectivity among brain regions which operates at the level of neuronal dynamics. We propose an alternative time-variant dynamic causal model (TV-DCM) where neuronal dynamic activity evolves based on generalized dynamic neural networks (GDNNs). The identification of brain architecture connectivity is carried out based on a least squares criterion and on a global search technique. Computer simulations carried out in the paper show that TV-DCM may provide multiple solutions, i.e. a set of different architectures all of which approximate the data equally well. Numerical comparisons between TV-DCM and DCM are also given. In order to determine the unique causal structure of brain regions, we apply an additional criterion, i.e. an estimation of generalization error, known from the theory of neural networks. Computer simulations also confirm the validity of our techniques.