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Abstract |
Current experimental techniques usually cannot probe the global interconnection pattern of a network. Thus, reconstructing or reverse-engineering the network topology of coupled nodes based upon observed data has become a very active research area. Most existing reconstruction methods are based on networks of oscillators with generally smooth dynamics. However, for nonlinear and non-smooth stochastic dynamical systems, e.g., neuronal networks, the reconstruction of the full topology remains a challenge. Here, we present a noninterventional reconstruction method, which is based on Granger causality theory, for the widely used conductance-based, integrate-and-fire type neuronal networks. For this nonlinear system, we have established a direct theoretical connection between Granger causal connectivity and structural connectivity. |
